definition of non directional hypothesis

Directional and non-directional hypothesis: A Comprehensive Guide

Karolina Konopka

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In the world of research and statistical analysis, hypotheses play a crucial role in formulating and testing scientific claims. Understanding the differences between directional and non-directional hypothesis is essential for designing sound experiments and drawing accurate conclusions. Whether you’re a student, researcher, or simply curious about the foundations of hypothesis testing, this guide will equip you with the knowledge and tools to navigate this fundamental aspect of scientific inquiry.

Understanding Directional Hypothesis

Understanding directional hypotheses is crucial for conducting hypothesis-driven research, as they guide the selection of appropriate statistical tests and aid in the interpretation of results. By incorporating directional hypotheses, researchers can make more precise predictions, contribute to scientific knowledge, and advance their fields of study.

Definition of directional hypothesis

Directional hypotheses, also known as one-tailed hypotheses, are statements in research that make specific predictions about the direction of a relationship or difference between variables. Unlike non-directional hypotheses, which simply state that there is a relationship or difference without specifying its direction, directional hypotheses provide a focused and precise expectation.

A directional hypothesis predicts either a positive or negative relationship between variables or predicts that one group will perform better than another. It asserts a specific direction of effect or outcome. For example, a directional hypothesis could state that “increased exposure to sunlight will lead to an improvement in mood” or “participants who receive the experimental treatment will exhibit higher levels of cognitive performance compared to the control group.”

Directional hypotheses are formulated based on existing theory, prior research, or logical reasoning, and they guide the researcher’s expectations and analysis. They allow for more targeted predictions and enable researchers to test specific hypotheses using appropriate statistical tests.

The role of directional hypothesis in research

Directional hypotheses also play a significant role in research surveys. Let’s explore their role specifically in the context of survey research:

• Objective-driven surveys : Directional hypotheses help align survey research with specific objectives. By formulating directional hypotheses, researchers can focus on gathering data that directly addresses the predicted relationship or difference between variables of interest.
• Question design and measurement : Directional hypotheses guide the design of survey question types and the selection of appropriate measurement scales. They ensure that the questions are tailored to capture the specific aspects related to the predicted direction, enabling researchers to obtain more targeted and relevant data from survey respondents.
• Data analysis and interpretation : Directional hypotheses assist in data analysis by directing researchers towards appropriate statistical tests and methods. Researchers can analyze the survey data to specifically test the predicted relationship or difference, enhancing the accuracy and reliability of their findings. The results can then be interpreted within the context of the directional hypothesis, providing more meaningful insights.
• Practical implications and decision-making : Directional hypotheses in surveys often have practical implications. When the predicted relationship or difference is confirmed, it informs decision-making processes, program development, or interventions. The survey findings based on directional hypotheses can guide organizations, policymakers, or practitioners in making informed choices to achieve desired outcomes.
• Replication and further research : Directional hypotheses in survey research contribute to the replication and extension of studies. Researchers can replicate the survey with different populations or contexts to assess the generalizability of the predicted relationships. Furthermore, if the directional hypothesis is supported, it encourages further research to explore underlying mechanisms or boundary conditions.

By incorporating directional hypotheses in survey research, researchers can align their objectives, design effective surveys, conduct focused data analysis, and derive practical insights. They provide a framework for organizing survey research and contribute to the accumulation of knowledge in the field.

Examples of research questions for directional hypothesis

Here are some examples of research questions that lend themselves to directional hypotheses:

• Does increased daily exercise lead to a decrease in body weight among sedentary adults?
• Is there a positive relationship between study hours and academic performance among college students?
• Does exposure to violent video games result in an increase in aggressive behavior among adolescents?
• Does the implementation of a mindfulness-based intervention lead to a reduction in stress levels among working professionals?
• Is there a difference in customer satisfaction between Product A and Product B, with Product A expected to have higher satisfaction ratings?
• Does the use of social media influence self-esteem levels, with higher social media usage associated with lower self-esteem?
• Is there a negative relationship between job satisfaction and employee turnover, indicating that lower job satisfaction leads to higher turnover rates?
• Does the administration of a specific medication result in a decrease in symptoms among individuals with a particular medical condition?
• Does increased access to early childhood education lead to improved cognitive development in preschool-aged children?
• Is there a difference in purchase intention between advertisements with celebrity endorsements and advertisements without, with celebrity endorsements expected to have a higher impact?

These research questions generate specific predictions about the direction of the relationship or difference between variables and can be tested using appropriate research methods and statistical analyses.

Definition of non-directional hypothesis

Non-directional hypotheses, also known as two-tailed hypotheses, are statements in research that indicate the presence of a relationship or difference between variables without specifying the direction of the effect. Instead of making predictions about the specific direction of the relationship or difference, non-directional hypotheses simply state that there is an association or distinction between the variables of interest.

Non-directional hypotheses are often used when there is no prior theoretical basis or clear expectation about the direction of the relationship. They leave the possibility open for either a positive or negative relationship, or for both groups to differ in some way without specifying which group will perform better or worse.

Advantages and utility of non-directional hypothesis

Non-directional hypotheses in survey s offer several advantages and utilities, providing flexibility and comprehensive analysis of survey data. Here are some of the key advantages and utilities of using non-directional hypotheses in surveys:

• Exploration of Relationships : Non-directional hypotheses allow researchers to explore and examine relationships between variables without assuming a specific direction. This is particularly useful in surveys where the relationship between variables may not be well-known or there may be conflicting evidence regarding the direction of the effect.
• Flexibility in Question Design : With non-directional hypotheses, survey questions can be designed to measure the relationship between variables without being biased towards a particular outcome. This flexibility allows researchers to collect data and analyze the results more objectively.
• Open to Unexpected Findings : Non-directional hypotheses enable researchers to be open to unexpected or surprising findings in survey data. By not committing to a specific direction of the effect, researchers can identify and explore relationships that may not have been initially anticipated, leading to new insights and discoveries.
• Comprehensive Analysis : Non-directional hypotheses promote comprehensive analysis of survey data by considering the possibility of an effect in either direction. Researchers can assess the magnitude and significance of relationships without limiting their analysis to only one possible outcome.
• S tatistical Validity : Non-directional hypotheses in surveys allow for the use of two-tailed statistical tests, which provide a more conservative and robust assessment of significance. Two-tailed tests consider both positive and negative deviations from the null hypothesis, ensuring accurate and reliable statistical analysis of survey data.
• Exploratory Research : Non-directional hypotheses are particularly useful in exploratory research, where the goal is to gather initial insights and generate hypotheses. Surveys with non-directional hypotheses can help researchers explore various relationships and identify patterns that can guide further research or hypothesis development.

It is worth noting that the choice between directional and non-directional hypotheses in surveys depends on the research objectives, existing knowledge, and the specific variables being investigated. Researchers should carefully consider the advantages and limitations of each approach and select the one that aligns best with their research goals and survey design.

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Directional vs Non-Directional Hypothesis: Key Difference

In statistics, a directional hypothesis, also known as a one-tailed hypothesis, is a type of hypothesis that predicts the direction of the relationship between variables or the direction of the difference between groups.

The introduction of a directional hypothesis in a research study provides an overview of the specific prediction being made about the relationship between variables or the difference between groups. It sets the stage for the research question and outlines the expected direction of the findings. The introduction typically includes the following elements:

Research Context: Begin by introducing the general topic or research area that the study is focused on. Provide background information and highlight the significance of the research question.

Research Question: Clearly state the specific research question that the study aims to answer. This question should be directly related to the variables being investigated.

Previous Research: Summarize relevant literature or previous studies that have explored similar or related topics. This helps establish the existing knowledge base and provides a rationale for the hypothesis.

Hypothesis Statement: Present the directional hypothesis clearly and concisely. State the predicted relationship between variables or the expected difference between groups. For example, if studying the impact of a new teaching method on student performance, a directional hypothesis could be, “Students who receive the new teaching method will demonstrate higher test scores compared to students who receive the traditional teaching method.”

Justification: Provide a logical explanation for the directional hypothesis based on the existing literature or theoretical framework . Discuss any previous findings, theories, or empirical evidence that support the predicted direction of the relationship or difference.

Objectives: Outline the specific objectives or aims of the study, which should align with the research question and hypothesis. These objectives help guide the research process and provide a clear focus for the study.

By including these elements in the introduction of a research study, the directional hypothesis is introduced effectively, providing a clear and justified prediction about the expected outcome of the research.

When formulating a directional hypothesis, researchers make a specific prediction about the expected relationship or difference between variables. They specify whether they expect an increase or decrease in the dependent variable, or whether one group will score higher or lower than another group

What is Directional Hypothesis?

With a correlational study, a directional hypothesis states that there is a positive (or negative) correlation between two variables. When a hypothesis states the direction of the results, it is referred to as a directional (one-tailed) hypothesis; this is because it states that the results go in one direction.

Definition:

A directional hypothesis is a one-tailed hypothesis that states the direction of the difference or relationship (e.g. boys are more helpful than girls).

Research Question: Does exercise have a positive impact on mood?

Directional Hypothesis: Engaging in regular exercise will result in an increase in positive mood compared to a sedentary lifestyle.

In this example, the directional hypothesis predicts that regular exercise will have a specific effect on mood, specifically leading to an increase in positive mood. The researcher expects that individuals who engage in regular exercise will experience improvements in their overall mood compared to individuals who lead a sedentary lifestyle.

It’s important to note that this is just one example, and directional hypotheses can be formulated in various research areas and contexts. The key is to make a specific prediction about the direction of the relationship or difference between variables based on prior knowledge or theoretical considerations.

Advantages of Directional Hypothesis

There are several advantages to using a directional hypothesis in research studies. Here are a few key benefits:

Specific Prediction:

A directional hypothesis allows researchers to make a specific prediction about the expected relationship or difference between variables. This provides a clear focus for the study and helps guide the research process. It also allows for more precise interpretation of the results.

Testable and Refutable:

Directional hypotheses can be tested and either supported or refuted by empirical evidence. Researchers can design their study and select appropriate statistical tests to specifically examine the predicted direction of the relationship or difference. This enhances the rigor and validity of the research.

Efficiency and Resource Allocation:

By making a specific prediction, researchers can allocate their resources more efficiently. They can focus on collecting data and conducting analyses that directly test the directional hypothesis, rather than exploring all possible directions or relationships. This can save time, effort, and resources.

Theory Development:

Directional hypotheses contribute to the development of theories and scientific knowledge. When a directional hypothesis is supported by empirical evidence, it provides support for existing theories or helps generate new theories. This advancement in knowledge can guide future research and understanding in the field.

Practical Applications:

Directional hypotheses can have practical implications and applications. If a hypothesis predicts a specific direction of change, such as the effectiveness of a treatment or intervention, it can inform decision-making and guide practical applications in fields such as medicine, psychology, or education.

Enhanced Communication:

Directional hypotheses facilitate clearer communication of research findings. When researchers have made specific predictions about the direction of the relationship or difference, they can effectively communicate their results to both academic and non-academic audiences. This promotes better understanding and application of the research outcomes.

It’s important to note that while directional hypotheses offer advantages, they also require stronger evidence to support them compared to non-directional hypotheses. Researchers should carefully consider the research context, existing literature, and theoretical considerations before formulating a directional hypothesis.

Disadvantages of Directional Hypothesis

While directional hypotheses have their advantages, there are also some potential disadvantages to consider:

Risk of Type I Error:

Directional hypotheses increase the risk of committing a Type I error , also known as a false positive. By focusing on a specific predicted direction, researchers may overlook the possibility of an opposite or null effect. If the actual relationship or difference does not align with the predicted direction, researchers may incorrectly conclude that there is no effect when, in fact, there may be.

Narrow Focus:

Directional hypotheses restrict the scope of investigation to a specific predicted direction. This narrow focus may overlook other potential relationships, nuances, or alternative explanations. Researchers may miss valuable insights or unexpected findings by excluding other possibilities from consideration.

Limited Generalizability:

Directional hypotheses may limit the generalizability of findings. If the study supports the predicted direction, the results may only apply to the specific context and conditions outlined in the hypothesis. Generalizing the findings to different populations, settings, or variables may require further research.

Biased Interpretation:

Directional hypotheses can introduce bias in the interpretation of results. Researchers may be inclined to selectively focus on evidence that supports the predicted direction while downplaying or ignoring contradictory evidence. This can hinder objectivity and lead to biased conclusions.

Increased Sample Size Requirements:

Directional hypotheses often require larger sample sizes compared to non-directional hypotheses. This is because statistical power needs to be sufficient to detect the predicted direction with a reasonable level of confidence. Larger samples can be more time-consuming and resource-intensive to obtain.

Reduced Flexibility:

Directional hypotheses limit flexibility in data analysis and statistical testing. Researchers may feel compelled to use specific statistical tests or analytical approaches that align with the predicted direction, potentially overlooking alternative methods that may be more appropriate or informative.

It’s important to weigh these disadvantages against the specific research context and objectives when deciding whether to use a directional hypothesis. In some cases, a non-directional hypothesis may be more suitable, allowing for a more exploratory and comprehensive investigation of the research question.

Non-Directional Hypothesis:

A non-directional hypothesis, also known as a two-tailed hypothesis, is a type of hypothesis that does not specify the direction of the relationship between variables or the difference between groups. Instead of predicting a specific direction, a non-directional hypothesis suggests that there will be a significant relationship or difference, without indicating whether it will be positive or negative, higher or lower, etc.

The introduction of a non-directional hypothesis in a research study provides an overview of the general prediction being made about the relationship between variables or the difference between groups, without specifying the direction. It sets the stage for the research question and outlines the expectation of a significant relationship or difference. The introduction typically includes the following elements:

Research Context:

Begin by introducing the general topic or research area that the study is focused on. Provide background information and highlight the significance of the research question.

Research Question:

Clearly state the specific research question that the study aims to answer. This question should be directly related to the variables being investigated.

Previous Research:

Summarize relevant literature or previous studies that have explored similar or related topics. This helps establish the existing knowledge base and provides a rationale for the hypothesis.

Hypothesis Statement:

Present the non-directional hypothesis clearly and concisely. State that there is an expected relationship or difference between variables or groups without specifying the direction. For example, if studying the relationship between socioeconomic status and academic achievement, a non-directional hypothesis could be, “There is a significant relationship between socioeconomic status and academic achievement.”

Justification:

Provide a logical explanation for the non-directional hypothesis based on the existing literature or theoretical framework. Discuss any previous findings, theories, or empirical evidence that support the notion of a relationship or difference between the variables or groups.

Objectives:

Outline the specific objectives or aims of the study, which should align with the research question and hypothesis. These objectives help guide the research process and provide a clear focus for the study.

By including these elements in the introduction of a research study, the non-directional hypothesis is introduced effectively, indicating the expectation of a significant relationship or difference without specifying the direction

What is Non-directional hypothesis?

In a non-directional hypothesis, researchers acknowledge that there may be an effect or relationship between variables but do not make a specific prediction about the direction of that effect. This allows for a more exploratory approach to data analysis and interpretation

If a hypothesis does not state a direction but simply says that one factor affects another, or that there is an association or correlation between two variables then it is called a non-directional (two-tailed) hypothesis.

Research Question: Is there a relationship between social media usage and self-esteem ?

Non-Directional Hypothesis: There is a significant relationship between social media usage and self-esteem.

In this example, the non-directional hypothesis suggests that there is a relationship between social media usage and self-esteem without specifying whether higher social media usage is associated with higher or lower self-esteem. The hypothesis acknowledges the possibility of an effect but does not make a specific prediction about the direction of that effect.

It’s important to note that this is just one example, and non-directional hypotheses can be formulated in various research areas and contexts. The key is to indicate the expectation of a significant relationship or difference without specifying the direction, allowing for a more exploratory approach to data analysis and interpretation.

Advantages of Non-directional hypothesis

Non-directional hypotheses, also known as two-tailed hypotheses, offer several advantages in research studies. Here are some of the key advantages:

Flexibility in Data Analysis:

Non-directional hypotheses allow for flexibility in data analysis. Researchers are not constrained by a specific predicted direction and can explore the relationship or difference in various ways. This flexibility enables a more comprehensive examination of the data, considering both positive and negative associations or differences.

Objective and Open-Minded Approach:

Non-directional hypotheses promote an objective and open-minded approach to research. Researchers do not have preconceived notions about the direction of the relationship or difference, which helps mitigate biases in data interpretation. They can objectively analyze the data without being influenced by their initial expectations.

Comprehensive Understanding:

By not specifying the direction, non-directional hypotheses facilitate a comprehensive understanding of the relationship or difference being investigated. Researchers can explore and consider all possible outcomes, leading to a more nuanced interpretation of the findings. This broader perspective can provide deeper insights into the research question.

Greater Sensitivity:

Non-directional hypotheses can be more sensitive to detecting unexpected or surprising relationships or differences. Researchers are not solely focused on confirming a specific predicted direction, but rather on uncovering any significant association or difference. This increased sensitivity allows for the identification of novel patterns and relationships that may have been overlooked with a directional hypothesis.

Replication and Generalizability:

Non-directional hypotheses support replication studies and enhance the generalizability of findings. By not restricting the investigation to a specific predicted direction, the results can be more applicable to different populations, contexts, or conditions. This broader applicability strengthens the validity and reliability of the research.

Hypothesis Generation:

Non-directional hypotheses can serve as a foundation for generating new hypotheses and research questions. Significant findings without a specific predicted direction can lead to further investigations and the formulation of more focused directional hypotheses in subsequent studies.

It’s important to consider the specific research context and objectives when deciding between a directional or non-directional hypothesis. Non-directional hypotheses are particularly useful when researchers are exploring new areas or when there is limited existing knowledge about the relationship or difference being studied.

Disadvantages of Non-directional hypothesis

Non-directional hypotheses have their advantages, there are also some potential disadvantages to consider:

Lack of Specificity: Non-directional hypotheses do not provide a specific prediction about the direction of the relationship or difference between variables. This lack of specificity may limit the interpretability and practical implications of the findings. Stakeholders may desire clear guidance on the expected direction of the effect.

Non-directional hypotheses often require larger sample sizes compared to directional hypotheses. This is because statistical power needs to be sufficient to detect any significant relationship or difference, regardless of the direction. Obtaining larger samples can be more time-consuming, resource-intensive, and costly.

Reduced Precision:

By not specifying the direction, non-directional hypotheses may result in less precise findings. Researchers may obtain statistically significant results indicating a relationship or difference, but the lack of direction may hinder their ability to understand the practical implications or mechanism behind the effect.

Potential for Post-hoc Interpretation:

Non-directional hypotheses can increase the risk of post-hoc interpretation of results. Researchers may be tempted to selectively interpret and highlight only the significant findings that support their preconceived notions or expectations, leading to biased interpretations.

Limited Theoretical Guidance:

Non-directional hypotheses may lack theoretical guidance in terms of understanding the underlying mechanisms or causal pathways. Without a specific predicted direction, it can be challenging to develop a comprehensive theoretical framework to explain the relationship or difference being studied.

Potential Missed Opportunities:

Non-directional hypotheses may limit the exploration of specific directions or subgroups within the data. By not focusing on a specific direction, researchers may miss important nuances or interactions that could contribute to a deeper understanding of the phenomenon under investigation.

It’s important to carefully consider the research question, available literature, and research objectives when deciding whether to use a non-directional hypothesis. Depending on the context and goals of the study, a non-directional hypothesis may be appropriate, but researchers should also be aware of the potential limitations and address them accordingly in their research design and interpretation of results.

Difference between directional and non-directional hypothesis

the main difference between a directional hypothesis and a non-directional hypothesis lies in the specificity of the prediction made about the relationship between variables or the difference between groups.

Directional Hypothesis:

A directional hypothesis, also known as a one-tailed hypothesis, makes a specific prediction about the direction of the relationship or difference. It states the expected outcome, whether it is a positive or negative relationship, a higher or lower value, an increase or decrease, etc. The directional hypothesis guides the research in a focused manner, specifying the direction to be tested.

Example: “Students who receive tutoring will demonstrate higher test scores compared to students who do not receive tutoring.”

A non-directional hypothesis, also known as a two-tailed hypothesis, does not specify the direction of the relationship or difference. It acknowledges the possibility of a relationship or difference between variables without predicting a specific direction. The non-directional hypothesis allows for exploration and analysis of both positive and negative associations or differences.

Example: “There is a significant relationship between sleep quality and academic performance.”

In summary, a directional hypothesis makes a specific prediction about the direction of the relationship or difference, while a non-directional hypothesis suggests a relationship or difference without specifying the direction. The choice between the two depends on the research question, existing literature, and the researcher’s objectives. Directional hypotheses provide a focused prediction, while non-directional hypotheses allow for more exploratory analysis .

When to use Directional Hypothesis?

A directional hypothesis is appropriate to use in specific situations where researchers have a clear theoretical or empirical basis for predicting the direction of the relationship or difference between variables. Here are some scenarios where a directional hypothesis is commonly employed:

Prior Research and Theoretical Framework: When previous studies, existing theories, or established empirical evidence strongly suggest a specific direction of the relationship or difference, a directional hypothesis can be formulated. Researchers can build upon the existing knowledge base and make a focused prediction based on this prior information.

Cause-and-Effect Relationships: In studies aiming to establish cause-and-effect relationships, directional hypotheses are often used. When there is a clear theoretical understanding of the causal relationship between variables, researchers can predict the expected direction of the effect based on the proposed mechanism.

Specific Research Objectives: If the research study has specific objectives that require a clear prediction about the direction, a directional hypothesis can be appropriate. For instance, if the aim is to test the effectiveness of a particular intervention or treatment, a directional hypothesis can guide the evaluation by predicting the expected positive or negative outcome.

Practical Applications: Directional hypotheses are useful when the research findings have direct practical implications. For example, in fields such as medicine, psychology, or education, researchers may formulate directional hypotheses to predict the effects of certain interventions or treatments on patient outcomes or educational achievement.

Hypothesis-Testing Approach: Researchers who adopt a hypothesis-testing approach, where they aim to confirm or disconfirm specific predictions, often use directional hypotheses. This approach involves formulating a specific hypothesis and conducting statistical tests to determine whether the data support or refute the predicted direction of the relationship or difference.

When to use non directional hypothesis?

A non-directional hypothesis, also known as a two-tailed hypothesis, is appropriate to use in several situations where researchers do not have a specific prediction about the direction of the relationship or difference between variables. Here are some scenarios where a non-directional hypothesis is commonly employed:

Exploratory Research:

When the research aims to explore a new area or investigate a relationship that has limited prior research or theoretical guidance, a non-directional hypothesis is often used. It allows researchers to gather initial data and insights without being constrained by a specific predicted direction.

Preliminary Studies:

Non-directional hypotheses are useful in preliminary or pilot studies that seek to gather preliminary evidence and generate hypotheses for further investigation. By using a non-directional hypothesis, researchers can gather initial data to inform the development of more specific hypotheses in subsequent studies.

Neutral Expectations:

If researchers have no theoretical or empirical basis to predict the direction of the relationship or difference, a non-directional hypothesis is appropriate. This may occur in situations where there is a lack of prior research, conflicting findings, or inconclusive evidence to support a specific direction.

Comparative Studies:

In studies where the objective is to compare two or more groups or conditions, a non-directional hypothesis is commonly used. The focus is on determining whether a significant difference exists, without making specific predictions about which group or condition will have higher or lower values.

Data-Driven Approach:

When researchers adopt a data-driven or exploratory approach to analysis, non-directional hypotheses are preferred. Instead of testing specific predictions, the aim is to explore the data, identify patterns, and generate hypotheses based on the observed relationships or differences.

Hypothesis-Generating Studies:

Non-directional hypotheses are often used in studies aimed at generating new hypotheses and research questions. By exploring associations or differences without specifying the direction, researchers can identify potential relationships or factors that can serve as a basis for future research.

Strategies to improve directional and non-directional hypothesis

To improve the quality of both directional and non-directional hypotheses, researchers can employ various strategies. Here are some strategies to enhance the formulation of hypotheses:

Strategies to Improve Directional Hypotheses:

Review existing literature:.

Conduct a thorough review of relevant literature to identify previous research findings, theories, and empirical evidence related to the variables of interest. This will help inform and support the formulation of a specific directional hypothesis based on existing knowledge.

Develop a Theoretical Framework:

Build a theoretical framework that outlines the expected causal relationship between variables. The theoretical framework should provide a clear rationale for predicting the direction of the relationship based on established theories or concepts.

Conduct Pilot Studies:

Conducting pilot studies or preliminary research can provide valuable insights and data to inform the formulation of a directional hypothesis. Initial findings can help researchers identify patterns or relationships that support a specific predicted direction.

Seek Expert Input:

Seek input from experts or colleagues in the field who have expertise in the area of study. Discuss the research question and hypothesis with them to obtain valuable insights, perspectives, and feedback that can help refine and improve the directional hypothesis.

Clearly Define Variables:

Clearly define and operationalize the variables in the hypothesis to ensure precision and clarity. This will help avoid ambiguity and ensure that the hypothesis is testable and measurable.

Strategies to Improve Non-Directional Hypotheses:

Preliminary exploration:.

Conduct initial exploratory research to gather preliminary data and insights on the relationship or difference between variables. This can provide a foundation for formulating a non-directional hypothesis based on observed patterns or trends.

Analyze Existing Data:

Analyze existing datasets to identify potential relationships or differences. Exploratory data analysis techniques such as data visualization, descriptive statistics, and correlation analysis can help uncover initial insights that can guide the formulation of a non-directional hypothesis.

Use Exploratory Research Designs:

Employ exploratory research designs such as qualitative studies, case studies, or grounded theory approaches. These designs allow researchers to gather rich data and explore relationships or differences without preconceived notions about the direction.

Consider Alternative Explanations:

When formulating a non-directional hypothesis, consider alternative explanations or potential factors that may influence the relationship or difference between variables. This can help ensure a comprehensive and nuanced understanding of the phenomenon under investigation.

Refine Based on Initial Findings:

Refine the non-directional hypothesis based on initial findings and observations from exploratory analyses. These findings can guide the formulation of more specific hypotheses in subsequent studies or inform the direction of further research.

In conclusion, both directional and non-directional hypotheses have their merits and are valuable in different research contexts.

 Here’s a summary of the key points regarding directional and non-directional hypotheses:

• A directional hypothesis makes a specific prediction about the direction of the relationship or difference between variables.
• It is appropriate when there is a clear theoretical or empirical basis for predicting the direction.
• Directional hypotheses provide a focused approach, guiding the research towards confirming or refuting a specific predicted direction.
• They are useful in studies where cause-and-effect relationships are being examined or when specific practical implications are desired.
• Directional hypotheses require careful consideration of prior research, theoretical frameworks, and available evidence.
• A non-directional hypothesis does not specify the direction of the relationship or difference between variables.
• It is employed when there is limited prior knowledge, conflicting findings, or a desire for exploratory analysis.
• Non-directional hypotheses allow for flexibility and open-mindedness in exploring the data, considering both positive and negative associations or differences.
• They are suitable for preliminary studies, exploratory research, or when the research question does not have a clear predicted direction.
• Non-directional hypotheses are beneficial for generating new hypotheses, replication studies, and enhancing generalizability.

In both cases, it is essential to ensure that hypotheses are clear, testable, and aligned with the research objectives. Researchers should also be open to revising and refining hypotheses based on the findings and feedback obtained during the research process. The choice between a directional and non-directional hypothesis depends on factors such as the research question, available literature, theoretical frameworks, and the specific objectives of the study. Researchers should carefully consider these factors to determine the most appropriate type of hypothesis to use in their research

Research Hypothesis In Psychology: Types, & Examples

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

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A research hypothesis, in its plural form “hypotheses,” is a specific, testable prediction about the anticipated results of a study, established at its outset. It is a key component of the scientific method .

Hypotheses connect theory to data and guide the research process towards expanding scientific understanding

Some key points about hypotheses:

• A hypothesis expresses an expected pattern or relationship. It connects the variables under investigation.
• It is stated in clear, precise terms before any data collection or analysis occurs. This makes the hypothesis testable.
• A hypothesis must be falsifiable. It should be possible, even if unlikely in practice, to collect data that disconfirms rather than supports the hypothesis.
• Hypotheses guide research. Scientists design studies to explicitly evaluate hypotheses about how nature works.
• For a hypothesis to be valid, it must be testable against empirical evidence. The evidence can then confirm or disprove the testable predictions.
• Hypotheses are informed by background knowledge and observation, but go beyond what is already known to propose an explanation of how or why something occurs.

Predictions typically arise from a thorough knowledge of the research literature, curiosity about real-world problems or implications, and integrating this to advance theory. They build on existing literature while providing new insight.

Types of Research Hypotheses

Alternative hypothesis.

The research hypothesis is often called the alternative or experimental hypothesis in experimental research.

It typically suggests a potential relationship between two key variables: the independent variable, which the researcher manipulates, and the dependent variable, which is measured based on those changes.

The alternative hypothesis states a relationship exists between the two variables being studied (one variable affects the other).

A hypothesis is a testable statement or prediction about the relationship between two or more variables. It is a key component of the scientific method. Some key points about hypotheses:

• Important hypotheses lead to predictions that can be tested empirically. The evidence can then confirm or disprove the testable predictions.

In summary, a hypothesis is a precise, testable statement of what researchers expect to happen in a study and why. Hypotheses connect theory to data and guide the research process towards expanding scientific understanding.

An experimental hypothesis predicts what change(s) will occur in the dependent variable when the independent variable is manipulated.

It states that the results are not due to chance and are significant in supporting the theory being investigated.

The alternative hypothesis can be directional, indicating a specific direction of the effect, or non-directional, suggesting a difference without specifying its nature. It’s what researchers aim to support or demonstrate through their study.

Null Hypothesis

The null hypothesis states no relationship exists between the two variables being studied (one variable does not affect the other). There will be no changes in the dependent variable due to manipulating the independent variable.

It states results are due to chance and are not significant in supporting the idea being investigated.

The null hypothesis, positing no effect or relationship, is a foundational contrast to the research hypothesis in scientific inquiry. It establishes a baseline for statistical testing, promoting objectivity by initiating research from a neutral stance.

Many statistical methods are tailored to test the null hypothesis, determining the likelihood of observed results if no true effect exists.

This dual-hypothesis approach provides clarity, ensuring that research intentions are explicit, and fosters consistency across scientific studies, enhancing the standardization and interpretability of research outcomes.

Nondirectional Hypothesis

A non-directional hypothesis, also known as a two-tailed hypothesis, predicts that there is a difference or relationship between two variables but does not specify the direction of this relationship.

It merely indicates that a change or effect will occur without predicting which group will have higher or lower values.

For example, “There is a difference in performance between Group A and Group B” is a non-directional hypothesis.

Directional Hypothesis

A directional (one-tailed) hypothesis predicts the nature of the effect of the independent variable on the dependent variable. It predicts in which direction the change will take place. (i.e., greater, smaller, less, more)

It specifies whether one variable is greater, lesser, or different from another, rather than just indicating that there’s a difference without specifying its nature.

For example, “Exercise increases weight loss” is a directional hypothesis.

Falsifiability

The Falsification Principle, proposed by Karl Popper , is a way of demarcating science from non-science. It suggests that for a theory or hypothesis to be considered scientific, it must be testable and irrefutable.

Falsifiability emphasizes that scientific claims shouldn’t just be confirmable but should also have the potential to be proven wrong.

It means that there should exist some potential evidence or experiment that could prove the proposition false.

However many confirming instances exist for a theory, it only takes one counter observation to falsify it. For example, the hypothesis that “all swans are white,” can be falsified by observing a black swan.

For Popper, science should attempt to disprove a theory rather than attempt to continually provide evidence to support a research hypothesis.

Can a Hypothesis be Proven?

Hypotheses make probabilistic predictions. They state the expected outcome if a particular relationship exists. However, a study result supporting a hypothesis does not definitively prove it is true.

All studies have limitations. There may be unknown confounding factors or issues that limit the certainty of conclusions. Additional studies may yield different results.

In science, hypotheses can realistically only be supported with some degree of confidence, not proven. The process of science is to incrementally accumulate evidence for and against hypothesized relationships in an ongoing pursuit of better models and explanations that best fit the empirical data. But hypotheses remain open to revision and rejection if that is where the evidence leads.

• Disproving a hypothesis is definitive. Solid disconfirmatory evidence will falsify a hypothesis and require altering or discarding it based on the evidence.
• However, confirming evidence is always open to revision. Other explanations may account for the same results, and additional or contradictory evidence may emerge over time.

We can never 100% prove the alternative hypothesis. Instead, we see if we can disprove, or reject the null hypothesis.

If we reject the null hypothesis, this doesn’t mean that our alternative hypothesis is correct but does support the alternative/experimental hypothesis.

Upon analysis of the results, an alternative hypothesis can be rejected or supported, but it can never be proven to be correct. We must avoid any reference to results proving a theory as this implies 100% certainty, and there is always a chance that evidence may exist which could refute a theory.

How to Write a Hypothesis

• Identify variables . The researcher manipulates the independent variable and the dependent variable is the measured outcome.
• Operationalized the variables being investigated . Operationalization of a hypothesis refers to the process of making the variables physically measurable or testable, e.g. if you are about to study aggression, you might count the number of punches given by participants.
• Decide on a direction for your prediction . If there is evidence in the literature to support a specific effect of the independent variable on the dependent variable, write a directional (one-tailed) hypothesis. If there are limited or ambiguous findings in the literature regarding the effect of the independent variable on the dependent variable, write a non-directional (two-tailed) hypothesis.
• Make it Testable : Ensure your hypothesis can be tested through experimentation or observation. It should be possible to prove it false (principle of falsifiability).
• Clear & concise language . A strong hypothesis is concise (typically one to two sentences long), and formulated using clear and straightforward language, ensuring it’s easily understood and testable.

Consider a hypothesis many teachers might subscribe to: students work better on Monday morning than on Friday afternoon (IV=Day, DV= Standard of work).

Now, if we decide to study this by giving the same group of students a lesson on a Monday morning and a Friday afternoon and then measuring their immediate recall of the material covered in each session, we would end up with the following:

• The alternative hypothesis states that students will recall significantly more information on a Monday morning than on a Friday afternoon.
• The null hypothesis states that there will be no significant difference in the amount recalled on a Monday morning compared to a Friday afternoon. Any difference will be due to chance or confounding factors.

More Examples

• Memory : Participants exposed to classical music during study sessions will recall more items from a list than those who studied in silence.
• Social Psychology : Individuals who frequently engage in social media use will report higher levels of perceived social isolation compared to those who use it infrequently.
• Developmental Psychology : Children who engage in regular imaginative play have better problem-solving skills than those who don’t.
• Clinical Psychology : Cognitive-behavioral therapy will be more effective in reducing symptoms of anxiety over a 6-month period compared to traditional talk therapy.
• Cognitive Psychology : Individuals who multitask between various electronic devices will have shorter attention spans on focused tasks than those who single-task.
• Health Psychology : Patients who practice mindfulness meditation will experience lower levels of chronic pain compared to those who don’t meditate.
• Organizational Psychology : Employees in open-plan offices will report higher levels of stress than those in private offices.
• Behavioral Psychology : Rats rewarded with food after pressing a lever will press it more frequently than rats who receive no reward.

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• v.37(16); 2022 Apr 25

A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

• EXAMPLE 1. Descriptive research question (quantitative research)
• - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
• “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
• RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
• EXAMPLE 2. Relationship research question (quantitative research)
• - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
• “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
• Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
• EXAMPLE 3. Comparative research question (quantitative research)
• - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
• “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
• RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
• STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
• EXAMPLE 4. Exploratory research question (qualitative research)
• - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
• “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
• EXAMPLE 5. Relationship research question (quantitative research)
• - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
• “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

• EXAMPLE 1. Working hypothesis (quantitative research)
• - A hypothesis that is initially accepted for further research to produce a feasible theory
• “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
• “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
• EXAMPLE 2. Exploratory hypothesis (qualitative research)
• - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
• “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
• “Conclusion
• Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
• EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
• “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
• Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
• EXAMPLE 4. Statistical hypothesis (quantitative research)
• - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
• “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
• “Statistical Analysis
• ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

• EXAMPLE 1. Background, hypotheses, and aims are provided
• “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
• “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
• “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
• EXAMPLE 2. Background, hypotheses, and aims are provided
• “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
• “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
• EXAMPLE 3. Background, aim, and hypothesis are provided
• “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
• “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
• “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Methodology: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ.

psychologyrocks

Hypotheses; directional and non-directional, what is the difference between an experimental and an alternative hypothesis.

Nothing much! If the study is a true experiment then we can call the hypothesis “an experimental hypothesis”, a prediction is made about how the IV causes an effect on the DV. In a study which does not involve the direct manipulation of an IV, i.e. a natural or quasi-experiment or any other quantitative research method (e.g. survey) has been used, then we call it an “alternative hypothesis”, it is the alternative to the null.

Directional hypothesis: A directional (or one-tailed hypothesis) states which way you think the results are going to go, for example in an experimental study we might say…”Participants who have been deprived of sleep for 24 hours will have more cold symptoms the week after exposure to a virus than participants who have not been sleep deprived”; the hypothesis compares the two groups/conditions and states which one will ….have more/less, be quicker/slower, etc.

If we had a correlational study, the directional hypothesis would state whether we expect a positive or a negative correlation, we are stating how the two variables will be related to each other, e.g. there will be a positive correlation between the number of stressful life events experienced in the last year and the number of coughs and colds suffered, whereby the more life events you have suffered the more coughs and cold you will have had”. The directional hypothesis can also state a negative correlation, e.g. the higher the number of face-book friends, the lower the life satisfaction score “

Non-directional hypothesis: A non-directional (or two tailed hypothesis) simply states that there will be a difference between the two groups/conditions but does not say which will be greater/smaller, quicker/slower etc. Using our example above we would say “There will be a difference between the number of cold symptoms experienced in the following week after exposure to a virus for those participants who have been sleep deprived for 24 hours compared with those who have not been sleep deprived for 24 hours.”

When the study is correlational, we simply state that variables will be correlated but do not state whether the relationship will be positive or negative, e.g. there will be a significant correlation between variable A and variable B.

Null hypothesis The null hypothesis states that the alternative or experimental hypothesis is NOT the case, if your experimental hypothesis was directional you would say…

Participants who have been deprived of sleep for 24 hours will NOT have more cold symptoms in the following week after exposure to a virus than participants who have not been sleep deprived and any difference that does arise will be due to chance alone.

or with a directional correlational hypothesis….

There will NOT be a positive correlation between the number of stress life events experienced in the last year and the number of coughs and colds suffered, whereby the more life events you have suffered the more coughs and cold you will have had”

With a non-directional or  two tailed hypothesis…

There will be NO difference between the number of cold symptoms experienced in the following week after exposure to a virus for those participants who have been sleep deprived for 24 hours compared with those who have not been sleep deprived for 24 hours.

or for a correlational …

there will be NO correlation between variable A and variable B.

When it comes to conducting an inferential stats test, if you have a directional hypothesis , you must do a one tailed test to find out whether your observed value is significant. If you have a non-directional hypothesis , you must do a two tailed test .

Exam Techniques/Advice

• Remember, a decent hypothesis will contain two variables, in the case of an experimental hypothesis there will be an IV and a DV; in a correlational hypothesis there will be two co-variables
• both variables need to be fully operationalised to score the marks, that is you need to be very clear and specific about what you mean by your IV and your DV; if someone wanted to repeat your study, they should be able to look at your hypothesis and know exactly what to change between the two groups/conditions and exactly what to measure (including any units/explanation of rating scales etc, e.g. “where 1 is low and 7 is high”)
• double check the question, did it ask for a directional or non-directional hypothesis?
• if you were asked for a null hypothesis, make sure you always include the phrase “and any difference/correlation (is your study experimental or correlational?) that does arise will be due to chance alone”

Practice Questions:

• Mr Faraz wants to compare the levels of attendance between his psychology group and those of Mr Simon, who teaches a different psychology group. Which of the following is a suitable directional (one tailed) hypothesis for Mr Faraz’s investigation?

A There will be a difference in the levels of attendance between the two psychology groups.

B Students’ level of attendance will be higher in Mr Faraz’s group than Mr Simon’s group.

C Any difference in the levels of attendance between the two psychology groups is due to chance.

D The level of attendance of the students will depend upon who is teaching the groups.

2. Tracy works for the local council. The council is thinking about reducing the number of people it employs to pick up litter from the street. Tracy has been asked to carry out a study to see if having the streets cleaned at less regular intervals will affect the amount of litter the public will drop. She studies a street to compare how much litter is dropped at two different times, once when it has just been cleaned and once after it has not been cleaned for a month.

Write a fully operationalised non-directional (two-tailed) hypothesis for Tracy’s study. (2)

3. Jamila is conducting a practical investigation to look at gender differences in carrying out visuo-spatial tasks. She decides to give males and females a jigsaw puzzle and will time them to see who completes it the fastest. She uses a random sample of pupils from a local school to get her participants.

(a) Write a fully operationalised directional (one tailed) hypothesis for Jamila’s study. (2) (b) Outline one strength and one weakness of the random sampling method. You may refer to Jamila’s use of this type of sampling in your answer. (4)

4. Which of the following is a non-directional (two tailed) hypothesis?

A There is a difference in driving ability with men being better drivers than women

B Women are better at concentrating on more than one thing at a time than men

C Women spend more time doing the cooking and cleaning than men

D There is a difference in the number of men and women who participate in sports

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Directional vs. Non-Directional Hypothesis in Research

In the world of research and statistical analysis, formulating hypotheses is a crucial step in the scientific process. Hypotheses guide researchers in making predictions and testing relationships between variables. When it comes to hypotheses, there are two main types: directional and non-directional.

Directional Hypothesis

A directional hypothesis, also known as a one-tailed hypothesis, is formulated with a specific predicted direction of the relationship between variables. It indicates an expectation of the relationship being either positive or negative.

Example of Directional Hypothesis

Advantages of directional hypothesis, non-directional hypothesis.

Non-directional hypotheses are often used when there is insufficient prior knowledge or theoretical basis to predict the direction of the relationship. It allows for a more exploratory approach, where the researcher is open to discovering the nature of the relationship through data analysis .

Example of Non-Directional Hypothesis

Read More: Population vs Sample | Examples

Advantages of Non-Directional Hypothesis:

Difference between directional and non-directional hypotheses.

Choosing Between Directional and Non-Directional Hypotheses: The choice between a directional and non-directional hypothesis depends on the research question, existing knowledge, and theoretical background. Here are a few considerations for selecting the appropriate type of hypothesis:

Directional vs. Non-Directional Hypothesis

Directional hypotheses offer specific predictions about the expected direction of the relationship, whereas non-directional hypotheses allow for more exploratory investigations without preconceived notions of the direction.

Remember, hypotheses serve as a roadmap for research, and regardless of their type, they play a crucial role in scientific inquiry and the pursuit of knowledge.

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Aims And Hypotheses, Directional And Non-Directional

March 7, 2021 - paper 2 psychology in context | research methods.

In Psychology, hypotheses are predictions made by the researcher about the outcome of a study. The research can chose to make a specific prediction about what they feel will happen in their research (a directional hypothesis) or they can make a ‘general,’ ‘less specific’ prediction about the outcome of their research (a non-directional hypothesis). The type of prediction that a researcher makes is usually dependent on whether or not any previous research has also investigated their research aim.

Variables Recap:

The  independent variable  (IV)  is the variable that psychologists  manipulate/change  to see if changing this variable has an effect on the  depen dent variable  (DV).

The  dependent variable (DV)  is the variable that the psychologists  measures  (to see if the IV has had an effect).

Research/Experimental Aim(S):

An aim is a clear and precise statement of the purpose of the study. It is a statement of why a research study is taking place. This should include what is being studied and what the study is trying to achieve. (e.g. “This study aims to investigate the effects of alcohol on reaction times”.

Hypotheses:

This is a testable statement that predicts what the researcher expects to happen in their research. The research study itself is therefore a means of testing whether or not the hypothesis is supported by the findings. If the findings do support the hypothesis then the hypothesis can be retained (i.e., accepted), but if not, then it must be rejected.

(1)  Directional Hypothesis:  states that the IV will have an effect on the DV and what that effect will be (the direction of results). For example, eating smarties will significantly  improve  an individual’s dancing ability. When writing a directional hypothesis, it is important that you state exactly  how  the IV will influence the DV.

(3)  A Null Hypothesis:  states that the IV will have no significant effect on the DV, for example, ‘eating smarties will have no effect in an individuals dancing ability.’

Understanding the fundamentals of a non-directional hypothesis

A crucial aspect of conducting research is formulating a hypothesis. In simple terms, a hypothesis is a statement that makes a prediction about the relationship between two or more variables. It serves as a guide for researchers to test their theories and draw conclusions. A non-directional hypothesis, also known as a two-tailed hypothesis, is a type of hypothesis that does not specify the direction of the predicted relationship between variables. This means that the researcher is not making a specific prediction and is open to the possibility of any outcome. In this article, we will delve into the fundamentals of a non-directional hypothesis and its significance in research studies.

The difference between directional and non-directional hypotheses

When conducting research, it is important to have a clear understanding of the different types of hypotheses. One key distinction to make is between directional and non-directional hypotheses.

A directional hypothesis, also known as a one-tailed hypothesis, predicts the direction of the relationship between variables. This means that the researcher has a specific prediction in mind. For example, a directional hypothesis might state that "increased exercise leads to improved cardiovascular health." In this case, the researcher is specifically predicting that increased exercise will have a positive effect on cardiovascular health.

On the other hand, a non-directional hypothesis, also known as a two-tailed hypothesis, does not specify the direction of the relationship between variables. This means that the researcher does not have a specific prediction in mind and is open to the possibility of any outcome. For example, a non-directional hypothesis might state that "there is a relationship between exercise and cardiovascular health." In this case, the researcher is simply predicting that there is some sort of relationship between the two variables, but is not specifying whether it is positive or negative.

The main difference between these two types of hypotheses lies in the level of specificity. A directional hypothesis provides a specific prediction about the relationship between variables, while a non-directional hypothesis leaves the prediction open-ended.

So why would a researcher choose to use a non-directional hypothesis? One reason is that it allows for more flexibility and open-mindedness in the research process. By not specifying a particular direction, the researcher is not constrained by preconceived notions or biases. This can lead to more objective and unbiased findings.

Another reason to use a non-directional hypothesis is when there is a lack of previous research or evidence to support a specific direction. If there is limited knowledge or conflicting results on the topic, it may be more appropriate to use a non-directional hypothesis to explore the relationship between variables without making any specific predictions.

In summary, the difference between directional and non-directional hypotheses lies in the level of specificity and prediction. While directional hypotheses specify the direction of the relationship, non-directional hypotheses leave the prediction open-ended. Non-directional hypotheses provide researchers with more flexibility and open-mindedness in the research process and can be especially useful in situations with limited previous research or conflicting evidence.

Why use a non-directional hypothesis

There are several reasons why researchers might choose to use a non-directional hypothesis in their studies. Firstly, it allows for more flexibility and open-mindedness in the research process. By not specifying a particular direction, researchers are not constrained by preconceived notions or biases. This can lead to more objective and unbiased findings, as the researcher is not influenced by their own expectations.

Another reason to use a non-directional hypothesis is when there is a lack of previous research or evidence to support a specific direction. Sometimes, researchers are exploring a new topic or are working in an area where there is limited knowledge or conflicting results. In these cases, it may be more appropriate to use a non-directional hypothesis to explore the relationship between variables without making any specific predictions. This allows for a more exploratory approach, where researchers can gather data and draw conclusions based on the evidence they find.

Additionally, using a non-directional hypothesis can help researchers avoid the problem of hindsight bias. Hindsight bias occurs when researchers interpret their results in a way that aligns with their initial expectations. By using a non-directional hypothesis, researchers are less likely to fall into this trap and can make more accurate interpretations of their data.

Overall, using a non-directional hypothesis can be beneficial in certain research scenarios. It allows for flexibility, open-mindedness, and exploration. It also helps researchers avoid bias and hindsight bias, leading to more objective and reliable findings. By using a non-directional hypothesis, researchers can approach their studies with a fresh perspective and make new discoveries in their field.

How to form a non-directional hypothesis

Forming a non-directional hypothesis is an essential step in conducting research. Unlike a directional hypothesis, which predicts the specific direction of the relationship between variables, a non-directional hypothesis leaves the prediction open-ended. This allows for more flexibility and exploration in the research process. Here are some key steps to consider when forming a non-directional hypothesis:

Identify the variables

Start by identifying the variables that you want to study. These variables should be measurable and have a logical connection to each other. For example, if you are interested in studying the relationship between exercise and cardiovascular health, your variables would be exercise and cardiovascular health.

Determine the type of relationship

Consider what type of relationship you want to explore between the variables. Are you looking for a relationship that is positive, negative, or simply any relationship? This will help guide the formation of your hypothesis. For example, if you want to explore any relationship between exercise and cardiovascular health, your hypothesis might be that there is a relationship between the two variables.

Keep it general

When writing your hypothesis, avoid specifying a particular direction of the relationship. Instead, keep it general and open-ended. This allows for more flexibility in the research process. For example, a non-directional hypothesis could state that "there is a relationship between exercise and cardiovascular health," without specifying whether it is positive or negative.

Be specific and testable

Although a non-directional hypothesis does not specify the direction of the relationship, it should still be specific and testable. This means that it should be clear enough to guide your research and allow you to gather data to support or reject the hypothesis. Make sure that your variables are clearly defined and that you have a clear plan for how you will measure them.

Consider alternative explanations

When forming a non-directional hypothesis, it is important to consider alternative explanations for the relationship between variables. This will help ensure that your hypothesis is comprehensive and takes into account different possibilities. For example, if you are studying the relationship between exercise and cardiovascular health, consider other factors that may influence cardiovascular health, such as diet or genetics.

By following these steps, you can effectively form a non-directional hypothesis for your research. This will allow you to explore the relationship between variables without being constrained by preconceived notions or biases. Remember to keep your hypothesis general, specific, and testable, and consider alternative explanations for the relationship.

Examples of non-directional hypotheses in research

When it comes to research, formulating a non-directional hypothesis can be particularly useful in certain scenarios. Let's explore some examples of non-directional hypotheses to understand how they can be applied in research studies.

"There is a relationship between sleep duration and academic performance."

This hypothesis suggests that there is some form of relationship between the amount of sleep an individual gets and their academic performance. However, it does not specify whether this relationship is positive or negative. By using a non-directional hypothesis, researchers can explore the potential impact of sleep duration on academic performance without making a specific prediction.

"There is a relationship between stress levels and job satisfaction." In this case, the hypothesis proposes that there is some form of relationship between stress levels and job satisfaction. However, it does not specify whether increased stress levels lead to decreased job satisfaction or vice versa. By using a non-directional hypothesis, researchers can examine the relationship between these variables without assuming a particular direction.

"There is a relationship between age and technology usage." This hypothesis suggests that there is some form of relationship between age and technology usage. However, it does not specify whether younger individuals are more likely to use technology or whether older individuals are more likely to use technology. By using a non-directional hypothesis, researchers can explore the potential relationship between age and technology usage without assuming a specific pattern.

These examples illustrate how non-directional hypotheses allow researchers to investigate relationships between variables without being tied to a specific prediction. This flexibility enables researchers to approach their studies with an open mind and gather objective data to draw conclusions.

By using non-directional hypotheses, researchers can explore uncharted territory, address conflicting evidence, and provide valuable insights into various fields of study. This approach promotes a more exploratory and unbiased research process, leading to more accurate and reliable findings. So, if you find yourself in a research scenario where a specific direction is unclear or the evidence is limited, consider using a non-directional hypothesis to guide your exploration.

Advantages and disadvantages of non-directional hypotheses

Non-directional hypotheses have several advantages and disadvantages that researchers should consider when conducting their studies. 

One of the main advantages of using a non-directional hypothesis is the flexibility it provides in the research process. By not specifying a particular direction, researchers are not limited by preconceived notions or biases.

This allows for more open-mindedness and exploration, which can lead to new insights and discoveries. It also allows researchers to consider alternative explanations and factors that may influence the relationship between variables.

Another advantage of non-directional hypotheses is that they help researchers avoid the problem of hindsight bias. Hindsight bias occurs when researchers interpret their results in a way that aligns with their initial expectations. By using a non-directional hypothesis, researchers are less likely to fall into this trap and can make more accurate interpretations of their data. This enhances the reliability and objectivity of their findings.

Non-directional hypotheses are also useful in situations where there is limited previous research or conflicting evidence. They allow researchers to explore the relationship between variables without making any specific predictions, which can be especially valuable in new or unexplored areas of study. This approach promotes a more exploratory and unbiased research process, enabling researchers to gather data and draw conclusions based on the evidence they find.

Disadvantages

However, there are also some disadvantages to using non-directional hypotheses. One disadvantage is that they may lack specificity. Without specifying the direction of the relationship, researchers may struggle to draw clear conclusions or make specific recommendations based on their findings. This can make it challenging to apply the research to practical situations or inform decision-making processes.

Another potential disadvantage is that non-directional hypotheses can be more difficult to test statistically. Without specifying the direction of the relationship, it may be harder to determine the significance of the findings or establish causal relationships between variables. This can make it more challenging to draw meaningful and robust conclusions from the research.

In summary, non-directional hypotheses offer flexibility, open-mindedness, and exploration in the research process. They help researchers avoid biases, and hindsight bias, and provide valuable insights in situations with limited previous research or conflicting evidence. However, they may lack specificity and can be more challenging to test statistically. Researchers should carefully consider these advantages and disadvantages when deciding whether to use a non-directional hypothesis in their studies.

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Non-Directional Hypothesis

A non-directional hypothesis is a two-tailed hypothesis that does not predict the direction of the difference or relationship (e.g. girls and boys are different in terms of helpfulness).

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5.2 - writing hypotheses.

The first step in conducting a hypothesis test is to write the hypothesis statements that are going to be tested. For each test you will have a null hypothesis (\(H_0\)) and an alternative hypothesis (\(H_a\)).

When writing hypotheses there are three things that we need to know: (1) the parameter that we are testing (2) the direction of the test (non-directional, right-tailed or left-tailed), and (3) the value of the hypothesized parameter.

• At this point we can write hypotheses for a single mean (\(\mu\)), paired means(\(\mu_d\)), a single proportion (\(p\)), the difference between two independent means (\(\mu_1-\mu_2\)), the difference between two proportions (\(p_1-p_2\)), a simple linear regression slope (\(\beta\)), and a correlation (\(\rho\)). 
• The research question will give us the information necessary to determine if the test is two-tailed (e.g., "different from," "not equal to"), right-tailed (e.g., "greater than," "more than"), or left-tailed (e.g., "less than," "fewer than").
• The research question will also give us the hypothesized parameter value. This is the number that goes in the hypothesis statements (i.e., \(\mu_0\) and \(p_0\)). For the difference between two groups, regression, and correlation, this value is typically 0.

Hypotheses are always written in terms of population parameters (e.g., \(p\) and \(\mu\)).  The tables below display all of the possible hypotheses for the parameters that we have learned thus far. Note that the null hypothesis always includes the equality (i.e., =).

Non Directional Hypothesis

Ai generator.

In the realm of hypothesis formulation, non-directional hypotheses offer a distinct perspective. These hypotheses suggest a relationship between variables without specifying the nature or direction of that relationship. This guide delves into non-directional hypothesis examples across various fields, outlines a step-by-step approach to crafting them, and provides expert tips to ensure your non-directional hypotheses are robust and insightful. Explore the world of Thesis statement hypotheses that explore connections without predetermined expectations.

What is the Non-Directional Hypothesis? – Definition

A non-directional hypothesis, also known as a two tailed hypothesis , is a type of hypothesis that predicts a relationship between variables without specifying the direction of that relationship. Unlike directional hypotheses that predict a specific outcome, non-directional hypotheses simply suggest that a relationship exists without indicating whether one variable will increase or decrease in response to changes in the other variable.

What is an Example of a Non-Directional Hypothesis Statement?

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“An increase in exercise frequency is associated with changes in weight.”

In this non-directional hypothesis, the statement suggests that a relationship exists between exercise frequency and weight changes but doesn’t specify whether increased exercise will lead to weight loss or weight gain. It leaves the direction of the relationship open for empirical investigation and data analysis.

100 Non Directional Hypothesis Statement Examples

Non-directional hypotheses explore relationships between variables without predicting the specific outcome. These simple hypothesis offer flexibility, allowing researchers to uncover unforeseen connections. Discover a range of non-directional hypothesis examples that span disciplines, enabling empirical exploration and evidence-based conclusions.

• Impact of Stress on Sleep Quality : Stress levels are related to changes in sleep quality among college students.
• Relationship Between Social Media Use and Loneliness : Social media use is associated with variations in reported feelings of loneliness.
• Connection Between Parenting Styles and Adolescent Self-Esteem : Different parenting styles correlate with differences in adolescent self-esteem levels.
• Effects of Temperature on Productivity : Temperature variations affect productivity levels in office environments.
• Link Between Screen Time and Eye Strain : Screen time is related to variations in reported eye strain among digital device users.
• Influence of Study Techniques on Exam Performance : Study techniques correlate with differences in exam performance among students.
• Relationship Between Classroom Environment and Student Engagement : Classroom environment is associated with variations in student engagement levels.
• Impact of Music Tempo on Heart Rate : Music tempo relates to changes in heart rate during exercise.
• Connection Between Diet and Cholesterol Levels : Dietary choices are related to variations in cholesterol levels among adults.
• Effects of Outdoor Exposure on Mood : Outdoor exposure is associated with changes in reported mood among urban dwellers.
• Relationship Between Personality Traits and Leadership Styles : Personality traits are associated with differences in preferred leadership styles among professionals.
• Impact of Time Management Strategies on Academic Performance : Time management strategies correlate with variations in academic performance among college students.
• Connection Between Cultural Exposure and Empathy Levels : Cultural exposure relates to changes in reported empathy levels among individuals.
• Effects of Nutrition Education on Dietary Choices : Nutrition education is associated with variations in dietary choices among adolescents.
• Link Between Social Support and Stress Levels : Social support is related to differences in reported stress levels among working adults.
• Influence of Exercise Intensity on Mood : Exercise intensity correlates with variations in reported mood among fitness enthusiasts.
• Relationship Between Parental Involvement and Academic Achievement : Parental involvement is associated with differences in academic achievement among schoolchildren.
• Impact of Sleep Duration on Cognitive Function : Sleep duration is related to changes in cognitive function among older adults.
• Connection Between Environmental Factors and Creativity : Environmental factors correlate with variations in reported creative thinking abilities among artists.
• Effects of Communication Styles on Conflict Resolution : Communication styles are associated with differences in conflict resolution outcomes among couples.
• Relationship Between Social Interaction and Life Satisfaction : Social interaction is related to variations in reported life satisfaction among elderly individuals.
• Impact of Classroom Seating Arrangements on Participation : Classroom seating arrangements correlate with differences in student participation levels.
• Connection Between Smartphone Use and Sleep Quality : Smartphone use is associated with changes in reported sleep quality among young adults.
• Effects of Mindfulness Practices on Stress Reduction : Mindfulness practices relate to variations in reported stress levels among participants.
• Link Between Gender and Communication Styles : Gender is related to differences in communication styles among individuals in group discussions.
• Influence of Advertising Exposure on Purchase Decisions : Advertising exposure correlates with variations in reported purchase decisions among consumers.
• Relationship Between Job Satisfaction and Employee Productivity : Job satisfaction is associated with differences in employee productivity levels.
• Impact of Social Support on Coping Mechanisms : Social support relates to variations in reported coping mechanisms among individuals facing challenges.
• Connection Between Classroom Environment and Student Creativity : Classroom environment is related to changes in student creativity levels.
• Effects of Exercise on Mood : Exercise is associated with variations in reported mood levels among participants.
• Relationship Between Music Preferences and Stress Levels : Music preferences are related to variations in reported stress levels among individuals.
• Impact of Nutrition Education on Food Choices : Nutrition education correlates with differences in dietary food choices among adolescents.
• Connection Between Physical Activity and Cognitive Function : Physical activity is associated with changes in cognitive function among older adults.
• Effects of Color Exposure on Mood : Color exposure relates to variations in reported mood levels among participants.
• Link Between Personality Traits and Career Choice : Personality traits are related to differences in career choices among individuals.
• Influence of Outdoor Recreation on Mental Well-being : Outdoor recreation is associated with variations in reported mental well-being among participants.
• Relationship Between Social Media Use and Self-Esteem : Social media use correlates with changes in reported self-esteem levels among young adults.
• Impact of Parenting Styles on Adolescent Risk Behavior : Parenting styles are related to variations in reported risk behaviors among adolescents.
• Connection Between Sleep Quality and Cognitive Performance : Sleep quality relates to changes in cognitive performance among students.
• Effects of Art Exposure on Creativity : Art exposure is associated with differences in reported creative thinking abilities among participants.
• Relationship Between Social Support and Mental Health : Social support is related to variations in reported mental health outcomes among individuals.
• Impact of Technology Use on Interpersonal Communication : Technology use correlates with differences in reported interpersonal communication skills among individuals.
• Connection Between Parental Attachment and Romantic Relationships : Parental attachment is associated with variations in the quality of romantic relationships among adults.
• Effects of Environmental Noise on Concentration : Environmental noise relates to changes in reported concentration levels among students.
• Link Between Music Exposure and Memory Performance : Music exposure is related to differences in memory performance among participants.
• Influence of Nutrition on Physical Fitness : Nutrition choices correlate with variations in reported physical fitness levels among athletes.
• Relationship Between Stress and Health Outcomes : Stress levels are associated with changes in reported health outcomes among individuals.
• Impact of Workplace Environment on Job Satisfaction : Workplace environment relates to differences in reported job satisfaction among employees.
• Connection Between Humor and Stress Reduction : Humor is related to variations in reported stress reduction among participants.
• Effects of Social Interaction on Emotional Well-being : Social interaction correlates with changes in reported emotional well-being among participants.
• Relationship Between Cultural Exposure and Cognitive Flexibility : Cultural exposure is related to variations in reported cognitive flexibility among individuals.
• Impact of Parent-Child Communication on Academic Achievement : Parent-child communication correlates with differences in academic achievement levels among students.
• Connection Between Personality Traits and Prosocial Behavior : Personality traits are associated with variations in reported prosocial behaviors among individuals.
• Effects of Nature Exposure on Stress Reduction : Nature exposure relates to changes in reported stress reduction among participants.
• Link Between Sleep Duration and Cognitive Performance : Sleep duration is related to differences in cognitive performance among participants.
• Influence of Social Media Use on Body Image : Social media use correlates with variations in reported body image satisfaction among young adults.
• Relationship Between Exercise and Mental Well-being : Exercise levels are associated with changes in reported mental well-being among participants.
• Impact of Cultural Competency Training on Patient Care : Cultural competency training relates to differences in patient care outcomes among healthcare professionals.
• Connection Between Perceived Social Support and Resilience : Perceived social support is related to variations in reported resilience levels among individuals.
• Effects of Environmental Factors on Mood : Environmental factors correlate with changes in reported mood levels among participants.
• Relationship Between Cultural Diversity and Team Performance : Cultural diversity is related to variations in reported team performance outcomes among professionals.
• Impact of Parental Involvement on Academic Motivation : Parental involvement correlates with differences in academic motivation levels among schoolchildren.
• Connection Between Mindfulness Practices and Anxiety Reduction : Mindfulness practices are associated with changes in reported anxiety levels among participants.
• Effects of Nutrition Education on Eating Habits : Nutrition education relates to variations in dietary eating habits among adolescents.
• Link Between Personality Traits and Learning Styles : Personality traits are related to differences in preferred learning styles among students.
• Influence of Nature Exposure on Creativity : Nature exposure correlates with variations in reported creative thinking abilities among individuals.
• Relationship Between Extracurricular Activities and Social Skills : Extracurricular activities are associated with changes in reported social skills among adolescents.
• Impact of Cultural Awareness Training on Stereotypes : Cultural awareness training relates to differences in perceived stereotypes among participants.
• Connection Between Sleep Quality and Emotional Regulation : Sleep quality is related to variations in reported emotional regulation skills among individuals.
• Effects of Music Exposure on Mood : Music exposure correlates with changes in reported mood levels among participants.
• Relationship Between Cultural Sensitivity and Cross-Cultural Communication : Cultural sensitivity is related to variations in reported cross-cultural communication skills among professionals.
• Impact of Parent-Child Bonding on Emotional Well-being : Parent-child bonding correlates with differences in reported emotional well-being levels among individuals.
• Connection Between Personality Traits and Conflict Resolution Styles : Personality traits are associated with variations in preferred conflict resolution styles among individuals.
• Effects of Mindfulness Practices on Focus and Concentration : Mindfulness practices relate to changes in reported focus and concentration levels among participants.
• Link Between Gender Identity and Career Aspirations : Gender identity is related to differences in reported career aspirations among individuals.
• Influence of Art Exposure on Emotional Expression : Art exposure correlates with variations in reported emotional expression abilities among participants.
• Relationship Between Peer Influence and Risky Behavior : Peer influence is associated with changes in reported engagement in risky behaviors among adolescents.
• Impact of Diversity Training on Workplace Harmony : Diversity training relates to differences in perceived workplace harmony among employees.
• Connection Between Sleep Patterns and Cognitive Performance : Sleep patterns are related to variations in cognitive performance among students.
• Effects of Exercise on Self-Esteem : Exercise correlates with changes in reported self-esteem levels among participants.
• Relationship Between Social Interaction and Well-being : Social interaction is related to variations in reported well-being levels among individuals.
• Impact of Parenting Styles on Adolescent Peer Relationships : Parenting styles correlate with differences in peer relationship quality among adolescents.
• Connection Between Personality Traits and Communication Effectiveness : Personality traits are associated with variations in communication effectiveness among professionals.
• Effects of Outdoor Activities on Stress Reduction : Outdoor activities relate to changes in reported stress reduction among participants.
• Link Between Music Exposure and Emotional Regulation : Music exposure is related to differences in reported emotional regulation skills among individuals.
• Influence of Family Dynamics on Academic Achievement : Family dynamics correlate with variations in academic achievement levels among students.
• Relationship Between Cultural Engagement and Empathy : Cultural engagement is associated with changes in reported empathy levels among individuals.
• Impact of Conflict Resolution Strategies on Relationship Satisfaction : Conflict resolution strategies relate to differences in reported relationship satisfaction levels among couples.
• Connection Between Sleep Quality and Physical Health : Sleep quality is related to variations in reported physical health outcomes among individuals.
• Effects of Social Support on Coping with Stress : Social support correlates with changes in reported coping strategies for stress among participants.
• Relationship Between Cultural Sensitivity and Patient Care : Cultural sensitivity is related to variations in reported patient care outcomes among healthcare professionals.
• Impact of Family Communication on Adolescent Well-being : Family communication correlates with differences in reported well-being levels among adolescents.
• Connection Between Personality Traits and Leadership Styles : Personality traits are associated with variations in preferred leadership styles among professionals.
• Effects of Nature Exposure on Attention Span : Nature exposure relates to changes in reported attention span among participants.
• Link Between Music Preference and Emotional Expression : Music preference is related to differences in reported emotional expression abilities among individuals.
• Influence of Peer Support on Academic Success : Peer support correlates with variations in reported academic success levels among students.
• Relationship Between Cultural Engagement and Creativity : Cultural engagement is associated with changes in reported creative thinking abilities among individuals.
• Impact of Conflict Resolution Skills on Relationship Satisfaction : Conflict resolution skills relate to differences in reported relationship satisfaction levels among couples.
• Connection Between Sleep Patterns and Stress Levels : Sleep patterns are related to variations in reported stress levels among individuals.
• Effects of Social Interaction on Happiness : Social interaction correlates with changes in reported happiness levels among participants.

Non-Directional Hypothesis Statement Examples for Psychology

These examples pertain to psychological studies and cover various relationships between psychological hypothesis concepts. For instance, the first example suggests that attachment styles might be related to romantic satisfaction, but it doesn’t specify whether attachment styles would increase or decrease satisfaction.

• Relationship Between Attachment Styles and Romantic Satisfaction : Attachment styles are related to variations in reported romantic satisfaction levels among individuals in psychology studies.
• Impact of Personality Traits on Career Success : Personality traits correlate with differences in reported career success outcomes among psychology study participants.
• Connection Between Parenting Styles and Adolescent Self-Esteem : Parenting styles are associated with variations in reported self-esteem levels among adolescents in psychological research.
• Effects of Social Media Use on Body Image : Social media use relates to changes in reported body image satisfaction among young adults in psychology experiments.
• Link Between Sleep Patterns and Emotional Well-being : Sleep patterns are related to differences in reported emotional well-being levels among psychology research participants.
• Influence of Mindfulness Practices on Stress Reduction : Mindfulness practices correlate with variations in reported stress reduction among psychology study participants.
• Relationship Between Social Interaction and Mental Health : Social interaction is associated with changes in reported mental health outcomes among individuals in psychology studies.
• Impact of Parent-Child Bonding on Emotional Resilience : Parent-child bonding relates to differences in reported emotional resilience levels among psychology research participants.
• Connection Between Cultural Sensitivity and Empathy : Cultural sensitivity is related to variations in reported empathy levels among individuals in psychology experiments.
• Effects of Exercise on Mood : Exercise correlates with changes in reported mood levels among psychology study participants.

Non-Directional Hypothesis Statement Examples in Research

These research hypothesis examples focus on research studies in general, covering a wide range of topics and relationships. For instance, the second example suggests that employee training might be related to workplace productivity, without indicating whether the training would lead to higher or lower productivity.

• Relationship Between Time Management and Academic Performance : Time management is related to variations in academic performance levels among research participants.
• Impact of Employee Training on Workplace Productivity : Employee training correlates with differences in reported workplace productivity outcomes among research subjects.
• Connection Between Media Exposure and Political Knowledge : Media exposure is associated with variations in reported political knowledge levels among research participants.
• Effects of Environmental Factors on Children’s Cognitive Development : Environmental factors relate to changes in reported cognitive development among research subjects.
• Link Between Parental Involvement and Student Motivation : Parental involvement is related to differences in reported student motivation levels among research participants.
• Influence of Cultural Immersion on Language Proficiency : Cultural immersion correlates with variations in reported language proficiency levels among research subjects.
• Relationship Between Leadership Styles and Team Performance : Leadership styles are associated with changes in reported team performance outcomes among research participants.
• Impact of Financial Literacy Education on Savings Habits : Financial literacy education relates to differences in reported savings habits among research subjects.
• Connection Between Stress Levels and Physical Health : Stress levels are related to variations in reported physical health outcomes among research participants.
• Effects of Music Exposure on Concentration : Music exposure correlates with changes in reported concentration levels among research subjects.

Non-Directional Hypothesis Statement Examples for Research Methodology

These examples are specific to the methods used in conducting research. The eighth example states that randomization might relate to group equivalence, but it doesn’t specify whether randomization would lead to more equivalent or less equivalent groups.

• Relationship Between Sampling Techniques and Research Validity : Sampling techniques are related to variations in research validity outcomes in studies of research methodology.
• Impact of Data Collection Methods on Data Accuracy : Data collection methods correlate with differences in reported data accuracy in research methodology experiments.
• Connection Between Research Design and Study Reproducibility : Research design is associated with variations in reported study reproducibility in research methodology studies.
• Effects of Questionnaire Format on Response Consistency : Questionnaire format relates to changes in reported response consistency in research methodology research.
• Link Between Ethical Considerations and Research Credibility : Ethical considerations are related to differences in reported research credibility in studies of research methodology.
• Influence of Measurement Scales on Data Precision : Measurement scales correlate with variations in reported data precision in research methodology experiments.
• Relationship Between Experimental Controls and Internal Validity : Experimental controls are associated with changes in internal validity outcomes in research methodology studies.
• Impact of Randomization on Group Equivalence : Randomization relates to differences in reported group equivalence in research methodology research.
• Connection Between Qualitative Data Analysis Methods and Data Richness : Qualitative data analysis methods are related to variations in reported data richness in studies of research methodology.
• Effects of Hypothesis Formulation on Research Focus : Hypothesis formulation correlates with changes in reported research focus in research methodology experiments.

These non-directional hypothesis statement examples offer insights into the diverse array of relationships explored in psychology, research, and research methodology studies, fostering empirical discovery and contributing to the advancement of knowledge across various fields.

Difference between Directional & Non-Directional Hypothesis

Directional and non-directional hypotheses are distinct approaches used in formulating hypotheses for research studies. Understanding the differences between them is essential for researchers to choose the appropriate type of causal hypothesis based on their study’s goals and prior knowledge.

• Direction: Directional hypotheses predict a specific relationship direction, while non-directional hypotheses do not specify a direction.
• Specificity: Directional hypotheses are more specific, while non-directional hypotheses are more general.
• Flexibility: Non-directional hypotheses allow for open-ended exploration, while directional hypotheses focus on confirming or refuting specific expectations.

How to Write a Non-Directional Hypothesis Statement – Step by Step Guide

• Identify Variables: Clearly define the variables you’re investigating—usually, an independent variable (the one manipulated) and a dependent variable (the one measured).
• Indicate Relationship: State that a relationship exists between the variables without predicting a specific direction.
• Use General Language: Craft the statement in a way that encompasses various possible outcomes.
• Avoid Biased Language: Do not include words that suggest a stronger effect or specific outcome for either variable.
• Connect to Research: If applicable, link the hypothesis to existing research or theories that justify exploring the relationship.

Tips for Writing a Non-Directional Hypothesis

• Start with Inquiry: Frame your hypothesis as an answer to a research question.
• Embrace Openness: Non-directional hypotheses are ideal when no strong expectation exists.
• Be Succinct: Keep the hypothesis statement concise and clear.
• Stay Neutral: Avoid implying that one variable will have a stronger impact.
• Allow Exploration: Leave room for various potential outcomes without preconceived notions.
• Tailor to Context: Ensure the hypothesis aligns with your research context and goals.

Non-directional hypotheses are particularly useful in exploratory research, where researchers aim to discover relationships without imposing specific expectations. They allow for unbiased investigation and the potential to uncover unexpected patterns or connections.

Remember that whether you choose a directional or non-directional hypothesis, both play critical roles in shaping the research process, guiding study design, data collection, and analysis. The choice depends on the research’s nature, goals, and existing knowledge in the field.  You may also be interested in our  science hypothesis .

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A research hypothesis is a statement that predicts or expects a relationship between variables, and it is tested through research. To create a hypothesis, researchers often review existing literature on the topic. This hypothesis is based on theories, observations, or empirical evidence. It guides the research process, including experiment design, data collection, and analysis. Ultimately, the hypothesis aims to predict the outcome of the study.

What is a Hypothesis in a Dissertation?

This article compares directional and non-directional hypotheses and provides guidelines for writing an effective hypothesis in research. The study explores the differences in predictions and research design implications between the two hypotheses.

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Types of hypothesis.

There are two main types of hypotheses in research:

Null Hypothesis (H0) 

The null hypothesis is the default assumption in statistical analysis that there is no significant relationship or effect between the variables being studied. It suggests that any observed differences or relationships are due to chance.

Alternative Hypothesis (Ha or H1)

The alternative hypothesis proposes a significant relationship or effect between variables, contradicting the null hypothesis. It reflects the researcher's expectations based on existing theories or observations.

What is Directional Hypotheses?

A directional hypothesis is a type of hypothesis that is used to predict a specific change or outcome in a research study. It is typically used when researchers have a clear idea of the direction in which they expect their results to go, either an increase or decrease, and want to test this prediction. By making a directional hypothesis, researchers can focus their research efforts and design studies that are more likely to uncover meaningful results. In essence, a directional hypothesis is a statement that predicts the direction of the change that is expected to occur between two groups or variables that are being investigated.

Examples of Directional Hypothesis

Example 1: Online versus Traditional Classroom Learning

For instance, consider a study comparing the average study time of college students in online courses versus those in traditional classroom settings. Drawing on prior research indicating that online learning might lead to reduced engagement, a potential directional hypothesis could be: "Students enrolled in online classes will spend fewer weekly study hours than those in traditional classrooms."

In this scenario, our hypothesis presents a clear expectation—that the average number of weekly study hours among online learners will be lower than that of traditional learners. If the actual findings reveal no significant difference or even higher study times among online learners, then our hypothesis would be refuted.

Example 2: Carbon Dioxide Levels and Global Warming

A directional hypothesis in this scenario would propose a specific change in direction between these two variables. For instance, a directional hypothesis might state that as carbon dioxide levels increase, global temperatures will also rise. This hypothesis suggests a causal relationship between the increase in CO2 levels and the phenomenon of global warming, indicating a direction of change in global temperatures corresponding to changes in CO2 levels.

What is a Non-Directional Hypotheses?

In scientific research, a non-directional hypothesis, or null hypothesis, is a statement that suggests the absence of a relationship or difference between the variables being studied. This type of hypothesis is used to test the validity of a research question by assuming that there is no significant effect or relationship between the variables under investigation. The null hypothesis is typically tested against an alternative hypothesis, which proposes that there is a significant effect or relationship between the variables. If the null hypothesis is rejected, it means that there is enough evidence to suggest that the alternative hypothesis is true, and the variables are indeed related or different from each other.

Non-Directional Hypothesis Example

Example: Is there a difference in anxiety levels between students who receive traditional classroom instruction and those who participate in online learning?

In this non-directional hypothesis, researchers are interested in understanding if there's a disparity in anxiety levels between students who are taught in traditional classrooms versus those who learn online. The non-directional hypothesis posits that there won't be any notable variance in anxiety levels between the two groups. This means that the researchers are not predicting whether one group will have higher or lower anxiety levels; rather, they are exploring if there's any difference at all.

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Directional vs. Non-Directional Hypotheses in Research

Both directional and non directional hypothesis have their place in research, and choosing the appropriate type depends on the research question being investigated. Researchers can use directional or non-directional hypotheses in their studies, depending on their specific expectations about the relationship between variables. A directional hypothesis predicts a specific direction of change, while a non-directional hypothesis predicts that there will be a difference between groups or conditions without specifying the direction of that difference. It's important to understand the difference between these types of hypotheses to conduct rigorous and insightful research. Directional hypotheses are useful when researchers want to test a specific expectation about the relationship between variables, while non-directional hypotheses are more appropriate when researchers simply want to test if there is any difference between groups or conditions.

How to Write an Effective Hypothesis in Research?

Writing an effective hypothesis involves several key steps to ensure clarity, testability, and relevance to the research question. Here's a guide on how to write an effective hypothesis:

• Identify the Research Question: Start by clearly defining the research question or problem you want to investigate. Your hypothesis should directly address this question.
• State the Null Hypothesis: The null hypothesis (H0) is a statement that there is no relationship or effect between the variables being studied. It serves as the default assumption and is typically stated as the absence of an effect or difference.
• Formulate the Alternative Hypothesis: The alternative hypothesis (H1 or Ha) is the statement that contradicts the null hypothesis and suggests that there is a relationship or effect between the variables. It reflects what you expect to find in your research.
• Make it Testable: Your hypothesis should be testable through empirical observation or experimentation. This means that there must be a way to collect data or evidence to support or refute the hypothesis.
• Be Specific and Clear: Clearly state the variables involved and the expected relationship between them. Avoid vague or ambiguous language to ensure that your hypothesis is easy to understand and interpret.
• Use Quantifiable Terms: Whenever possible, use quantifiable terms or measurable variables in your hypothesis. This makes it easier to collect data and analyze results objectively.
• Consider the Scope: Ensure that your hypothesis is focused and specific to the research hypothesis at hand. Avoid making broad generalizations that are difficult to test or validate.
• Revise and Refine: Once you've drafted your hypothesis, review it carefully to ensure accuracy and coherence. Revise as needed to clarify any ambiguities or inconsistencies.

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In conclusion, directional hypotheses predict whether variables will increase or decrease, providing a definite expectation about the direction of the relationship under investigation. Non-directional hypotheses, on the other hand, only claim that there is a difference between variables without specifying the direction of the change, leaving it open to any possibility. Both types of hypotheses play an important role in guiding research investigations and developing testable predictions.

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Directional Hypothesis: Definition and 10 Examples

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A directional hypothesis refers to a type of hypothesis used in statistical testing that predicts a particular direction of the expected relationship between two variables.

In simpler terms, a directional hypothesis is an educated, specific guess about the direction of an outcome—whether an increase, decrease, or a proclaimed difference in variable sets.

For example, in a study investigating the effects of sleep deprivation on cognitive performance, a directional hypothesis might state that as sleep deprivation (Independent Variable) increases, cognitive performance (Dependent Variable) decreases (Killgore, 2010). Such a hypothesis offers a clear, directional relationship whereby a specific increase or decrease is anticipated.

Global warming provides another notable example of a directional hypothesis. A researcher might hypothesize that as carbon dioxide (CO2) levels increase, global temperatures also increase (Thompson, 2010). In this instance, the hypothesis clearly articulates an upward trend for both variables. 

In any given circumstance, it’s imperative that a directional hypothesis is grounded on solid evidence. For instance, the CO2 and global temperature relationship is based on substantial scientific evidence, and not on a random guess or mere speculation (Florides & Christodoulides, 2009).

Directional vs Non-Directional vs Null Hypotheses

A directional hypothesis is generally contrasted to a non-directional hypothesis. Here’s how they compare:

• Directional hypothesis: A directional hypothesis provides a perspective of the expected relationship between variables, predicting the direction of that relationship (either positive, negative, or a specific difference). 
• Non-directional hypothesis: A non-directional hypothesis denotes the possibility of a relationship between two variables ( the independent and dependent variables ), although this hypothesis does not venture a prediction as to the direction of this relationship (Ali & Bhaskar, 2016). For example, a non-directional hypothesis might state that there exists a relationship between a person’s diet (independent variable) and their mood (dependent variable), without indicating whether improvement in diet enhances mood positively or negatively. Overall, the choice between a directional or non-directional hypothesis depends on the known or anticipated link between the variables under consideration in research studies.

Another very important type of hypothesis that we need to know about is a null hypothesis :

• Null hypothesis : The null hypothesis stands as a universality—the hypothesis that there is no observed effect in the population under study, meaning there is no association between variables (or that the differences are down to chance). For instance, a null hypothesis could be constructed around the idea that changing diet (independent variable) has no discernible effect on a person’s mood (dependent variable) (Yan & Su, 2016). This proposition is the one that we aim to disprove in an experiment.

While directional and non-directional hypotheses involve some integrated expectations about the outcomes (either distinct direction or a vague relationship), a null hypothesis operates on the premise of negating such relationships or effects.

The null hypotheses is typically proposed to be negated or disproved by statistical tests, paving way for the acceptance of an alternate hypothesis (either directional or non-directional).

Directional Hypothesis Examples

1. exercise and heart health.

Research suggests that as regular physical exercise (independent variable) increases, the risk of heart disease (dependent variable) decreases (Jakicic, Davis, Rogers, King, Marcus, Helsel, Rickman, Wahed, Belle, 2016). In this example, a directional hypothesis anticipates that the more individuals maintain routine workouts, the lesser would be their odds of developing heart-related disorders. This assumption is based on the underlying fact that routine exercise can help reduce harmful cholesterol levels, regulate blood pressure, and bring about overall health benefits. Thus, a direction – a decrease in heart disease – is expected in relation with an increase in exercise. 

2. Screen Time and Sleep Quality

Another classic instance of a directional hypothesis can be seen in the relationship between the independent variable, screen time (especially before bed), and the dependent variable, sleep quality. This hypothesis predicts that as screen time before bed increases, sleep quality decreases (Chang, Aeschbach, Duffy, Czeisler, 2015). The reasoning behind this hypothesis is the disruptive effect of artificial light (especially blue light from screens) on melatonin production, a hormone needed to regulate sleep. As individuals spend more time exposed to screens before bed, it is predictably hypothesized that their sleep quality worsens. 

3. Job Satisfaction and Employee Turnover

A typical scenario in organizational behavior research posits that as job satisfaction (independent variable) increases, the rate of employee turnover (dependent variable) decreases (Cheng, Jiang, & Riley, 2017). This directional hypothesis emphasizes that an increased level of job satisfaction would lead to a reduced rate of employees leaving the company. The theoretical basis for this hypothesis is that satisfied employees often tend to be more committed to the organization and are less likely to seek employment elsewhere, thus reducing turnover rates.

4. Healthy Eating and Body Weight

Healthy eating, as the independent variable, is commonly thought to influence body weight, the dependent variable, in a positive way. For example, the hypothesis might state that as consumption of healthy foods increases, an individual’s body weight decreases (Framson, Kristal, Schenk, Littman, Zeliadt, & Benitez, 2009). This projection is based on the premise that healthier foods, such as fruits and vegetables, are generally lower in calories than junk food, assisting in weight management.

5. Sun Exposure and Skin Health

The association between sun exposure (independent variable) and skin health (dependent variable) allows for a definitive hypothesis declaring that as sun exposure increases, the risk of skin damage or skin cancer increases (Whiteman, Whiteman, & Green, 2001). The premise aligns with the understanding that overexposure to the sun’s ultraviolet rays can deteriorate skin health, leading to conditions like sunburn or, in extreme cases, skin cancer.

6. Study Hours and Academic Performance

A regularly assessed relationship in academia suggests that as the number of study hours (independent variable) rises, so too does academic performance (dependent variable) (Nonis, Hudson, Logan, Ford, 2013). The hypothesis proposes a positive correlation , with an increase in study time expected to contribute to enhanced academic outcomes.

7. Screen Time and Eye Strain

It’s commonly hypothesized that as screen time (independent variable) increases, the likelihood of experiencing eye strain (dependent variable) also increases (Sheppard & Wolffsohn, 2018). This is based on the idea that prolonged engagement with digital screens—computers, tablets, or mobile phones—can cause discomfort or fatigue in the eyes, attributing to symptoms of eye strain.

8. Physical Activity and Stress Levels

In the sphere of mental health, it’s often proposed that as physical activity (independent variable) increases, levels of stress (dependent variable) decrease (Stonerock, Hoffman, Smith, Blumenthal, 2015). Regular exercise is known to stimulate the production of endorphins, the body’s natural mood elevators, helping to alleviate stress.

9. Water Consumption and Kidney Health

A common health-related hypothesis might predict that as water consumption (independent variable) increases, the risk of kidney stones (dependent variable) decreases (Curhan, Willett, Knight, & Stampfer, 2004). Here, an increase in water intake is inferred to reduce the risk of kidney stones by diluting the substances that lead to stone formation.

10. Traffic Noise and Sleep Quality

In urban planning research, it’s often supposed that as traffic noise (independent variable) increases, sleep quality (dependent variable) decreases (Muzet, 2007). Increased noise levels, particularly during the night, can result in sleep disruptions, thus, leading to poor sleep quality.

11. Sugar Consumption and Dental Health

In the field of dental health, an example might be stating as one’s sugar consumption (independent variable) increases, dental health (dependent variable) decreases (Sheiham, & James, 2014). This stems from the fact that sugar is a major factor in tooth decay, and increased consumption of sugary foods or drinks leads to a decline in dental health due to the high likelihood of cavities.

See 15 More Examples of Hypotheses Here

A directional hypothesis plays a critical role in research, paving the way for specific predicted outcomes based on the relationship between two variables. These hypotheses clearly illuminate the expected direction—the increase or decrease—of an effect. From predicting the impacts of healthy eating on body weight to forecasting the influence of screen time on sleep quality, directional hypotheses allow for targeted and strategic examination of phenomena. In essence, directional hypotheses provide the crucial path for inquiry, shaping the trajectory of research studies and ultimately aiding in the generation of insightful, relevant findings.

Ali, S., & Bhaskar, S. (2016). Basic statistical tools in research and data analysis. Indian Journal of Anaesthesia, 60 (9), 662-669. doi: https://doi.org/10.4103%2F0019-5049.190623  

Chang, A. M., Aeschbach, D., Duffy, J. F., & Czeisler, C. A. (2015). Evening use of light-emitting eReaders negatively affects sleep, circadian timing, and next-morning alertness. Proceeding of the National Academy of Sciences, 112 (4), 1232-1237. doi: https://doi.org/10.1073/pnas.1418490112  

Cheng, G. H. L., Jiang, D., & Riley, J. H. (2017). Organizational commitment and intrinsic motivation of regular and contractual primary school teachers in China. New Psychology, 19 (3), 316-326. Doi: https://doi.org/10.4103%2F2249-4863.184631  

Curhan, G. C., Willett, W. C., Knight, E. L., & Stampfer, M. J. (2004). Dietary factors and the risk of incident kidney stones in younger women: Nurses’ Health Study II. Archives of Internal Medicine, 164 (8), 885–891.

Florides, G. A., & Christodoulides, P. (2009). Global warming and carbon dioxide through sciences. Environment international , 35 (2), 390-401. doi: https://doi.org/10.1016/j.envint.2008.07.007

Framson, C., Kristal, A. R., Schenk, J. M., Littman, A. J., Zeliadt, S., & Benitez, D. (2009). Development and validation of the mindful eating questionnaire. Journal of the American Dietetic Association, 109 (8), 1439-1444. doi: https://doi.org/10.1016/j.jada.2009.05.006  

Jakicic, J. M., Davis, K. K., Rogers, R. J., King, W. C., Marcus, M. D., Helsel, D., … & Belle, S. H. (2016). Effect of wearable technology combined with a lifestyle intervention on long-term weight loss: The IDEA randomized clinical trial. JAMA, 316 (11), 1161-1171.

Khan, S., & Iqbal, N. (2013). Study of the relationship between study habits and academic achievement of students: A case of SPSS model. Higher Education Studies, 3 (1), 14-26.

Killgore, W. D. (2010). Effects of sleep deprivation on cognition. Progress in brain research , 185 , 105-129. doi: https://doi.org/10.1016/B978-0-444-53702-7.00007-5  

Marczinski, C. A., & Fillmore, M. T. (2014). Dissociative antagonistic effects of caffeine on alcohol-induced impairment of behavioral control. Experimental and Clinical Psychopharmacology, 22 (4), 298–311. doi: https://psycnet.apa.org/doi/10.1037/1064-1297.11.3.228  

Muzet, A. (2007). Environmental Noise, Sleep and Health. Sleep Medicine Reviews, 11 (2), 135-142. doi: https://doi.org/10.1016/j.smrv.2006.09.001  

Nonis, S. A., Hudson, G. I., Logan, L. B., & Ford, C. W. (2013). Influence of perceived control over time on college students’ stress and stress-related outcomes. Research in Higher Education, 54 (5), 536-552. doi: https://doi.org/10.1023/A:1018753706925  

Sheiham, A., & James, W. P. (2014). A new understanding of the relationship between sugars, dental caries and fluoride use: implications for limits on sugars consumption. Public health nutrition, 17 (10), 2176-2184. Doi: https://doi.org/10.1017/S136898001400113X  

Sheppard, A. L., & Wolffsohn, J. S. (2018). Digital eye strain: prevalence, measurement and amelioration. BMJ open ophthalmology , 3 (1), e000146. doi: http://dx.doi.org/10.1136/bmjophth-2018-000146

Stonerock, G. L., Hoffman, B. M., Smith, P. J., & Blumenthal, J. A. (2015). Exercise as Treatment for Anxiety: Systematic Review and Analysis. Annals of Behavioral Medicine, 49 (4), 542–556. doi: https://doi.org/10.1007/s12160-014-9685-9  

Thompson, L. G. (2010). Climate change: The evidence and our options. The Behavior Analyst , 33 , 153-170. Doi: https://doi.org/10.1007/BF03392211  

Whiteman, D. C., Whiteman, C. A., & Green, A. C. (2001). Childhood sun exposure as a risk factor for melanoma: a systematic review of epidemiologic studies. Cancer Causes & Control, 12 (1), 69-82. doi: https://doi.org/10.1023/A:1008980919928

Yan, X., & Su, X. (2009). Linear regression analysis: theory and computing . New Jersey: World Scientific.

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What is a Research Hypothesis: How to Write it, Types, and Examples

Any research begins with a research question and a research hypothesis . A research question alone may not suffice to design the experiment(s) needed to answer it. A hypothesis is central to the scientific method. But what is a hypothesis ? A hypothesis is a testable statement that proposes a possible explanation to a phenomenon, and it may include a prediction. Next, you may ask what is a research hypothesis ? Simply put, a research hypothesis is a prediction or educated guess about the relationship between the variables that you want to investigate.  

It is important to be thorough when developing your research hypothesis. Shortcomings in the framing of a hypothesis can affect the study design and the results. A better understanding of the research hypothesis definition and characteristics of a good hypothesis will make it easier for you to develop your own hypothesis for your research. Let’s dive in to know more about the types of research hypothesis , how to write a research hypothesis , and some research hypothesis examples .  

Table of Contents

What is a hypothesis ?  

A hypothesis is based on the existing body of knowledge in a study area. Framed before the data are collected, a hypothesis states the tentative relationship between independent and dependent variables, along with a prediction of the outcome.  

What is a research hypothesis ?  

Young researchers starting out their journey are usually brimming with questions like “ What is a hypothesis ?” “ What is a research hypothesis ?” “How can I write a good research hypothesis ?”   

A research hypothesis is a statement that proposes a possible explanation for an observable phenomenon or pattern. It guides the direction of a study and predicts the outcome of the investigation. A research hypothesis is testable, i.e., it can be supported or disproven through experimentation or observation.     

Characteristics of a good hypothesis  

Here are the characteristics of a good hypothesis :  

• Clearly formulated and free of language errors and ambiguity  
• Concise and not unnecessarily verbose  
• Has clearly defined variables  
• Testable and stated in a way that allows for it to be disproven  
• Can be tested using a research design that is feasible, ethical, and practical   
• Specific and relevant to the research problem  
• Rooted in a thorough literature search  
• Can generate new knowledge or understanding.  

How to create an effective research hypothesis  

A study begins with the formulation of a research question. A researcher then performs background research. This background information forms the basis for building a good research hypothesis . The researcher then performs experiments, collects, and analyzes the data, interprets the findings, and ultimately, determines if the findings support or negate the original hypothesis.  

Let’s look at each step for creating an effective, testable, and good research hypothesis :  

• Identify a research problem or question: Start by identifying a specific research problem.   
• Review the literature: Conduct an in-depth review of the existing literature related to the research problem to grasp the current knowledge and gaps in the field.   
• Formulate a clear and testable hypothesis : Based on the research question, use existing knowledge to form a clear and testable hypothesis . The hypothesis should state a predicted relationship between two or more variables that can be measured and manipulated. Improve the original draft till it is clear and meaningful.  
• State the null hypothesis: The null hypothesis is a statement that there is no relationship between the variables you are studying.   
• Define the population and sample: Clearly define the population you are studying and the sample you will be using for your research.  
• Select appropriate methods for testing the hypothesis: Select appropriate research methods, such as experiments, surveys, or observational studies, which will allow you to test your research hypothesis .  

Remember that creating a research hypothesis is an iterative process, i.e., you might have to revise it based on the data you collect. You may need to test and reject several hypotheses before answering the research problem.  

How to write a research hypothesis  

When you start writing a research hypothesis , you use an “if–then” statement format, which states the predicted relationship between two or more variables. Clearly identify the independent variables (the variables being changed) and the dependent variables (the variables being measured), as well as the population you are studying. Review and revise your hypothesis as needed.  

An example of a research hypothesis in this format is as follows:  

“ If [athletes] follow [cold water showers daily], then their [endurance] increases.”  

Population: athletes  

Independent variable: daily cold water showers  

Dependent variable: endurance  

You may have understood the characteristics of a good hypothesis . But note that a research hypothesis is not always confirmed; a researcher should be prepared to accept or reject the hypothesis based on the study findings.  

Research hypothesis checklist  

Following from above, here is a 10-point checklist for a good research hypothesis :  

• Testable: A research hypothesis should be able to be tested via experimentation or observation.  
• Specific: A research hypothesis should clearly state the relationship between the variables being studied.  
• Based on prior research: A research hypothesis should be based on existing knowledge and previous research in the field.  
• Falsifiable: A research hypothesis should be able to be disproven through testing.  
• Clear and concise: A research hypothesis should be stated in a clear and concise manner.  
• Logical: A research hypothesis should be logical and consistent with current understanding of the subject.  
• Relevant: A research hypothesis should be relevant to the research question and objectives.  
• Feasible: A research hypothesis should be feasible to test within the scope of the study.  
• Reflects the population: A research hypothesis should consider the population or sample being studied.  
• Uncomplicated: A good research hypothesis is written in a way that is easy for the target audience to understand.  

By following this research hypothesis checklist , you will be able to create a research hypothesis that is strong, well-constructed, and more likely to yield meaningful results.  

Types of research hypothesis  

Different types of research hypothesis are used in scientific research:  

1. Null hypothesis:

A null hypothesis states that there is no change in the dependent variable due to changes to the independent variable. This means that the results are due to chance and are not significant. A null hypothesis is denoted as H0 and is stated as the opposite of what the alternative hypothesis states.   

Example: “ The newly identified virus is not zoonotic .”  

2. Alternative hypothesis:

This states that there is a significant difference or relationship between the variables being studied. It is denoted as H1 or Ha and is usually accepted or rejected in favor of the null hypothesis.  

Example: “ The newly identified virus is zoonotic .”  

3. Directional hypothesis :

This specifies the direction of the relationship or difference between variables; therefore, it tends to use terms like increase, decrease, positive, negative, more, or less.   

Example: “ The inclusion of intervention X decreases infant mortality compared to the original treatment .”   

4. Non-directional hypothesis:

While it does not predict the exact direction or nature of the relationship between the two variables, a non-directional hypothesis states the existence of a relationship or difference between variables but not the direction, nature, or magnitude of the relationship. A non-directional hypothesis may be used when there is no underlying theory or when findings contradict previous research.  

Example, “ Cats and dogs differ in the amount of affection they express .”  

5. Simple hypothesis :

A simple hypothesis only predicts the relationship between one independent and another independent variable.  

Example: “ Applying sunscreen every day slows skin aging .”  

6 . Complex hypothesis :

A complex hypothesis states the relationship or difference between two or more independent and dependent variables.   

Example: “ Applying sunscreen every day slows skin aging, reduces sun burn, and reduces the chances of skin cancer .” (Here, the three dependent variables are slowing skin aging, reducing sun burn, and reducing the chances of skin cancer.)  

7. Associative hypothesis:  

An associative hypothesis states that a change in one variable results in the change of the other variable. The associative hypothesis defines interdependency between variables.  

Example: “ There is a positive association between physical activity levels and overall health .”  

8 . Causal hypothesis:

A causal hypothesis proposes a cause-and-effect interaction between variables.  

Example: “ Long-term alcohol use causes liver damage .”  

Note that some of the types of research hypothesis mentioned above might overlap. The types of hypothesis chosen will depend on the research question and the objective of the study.  

Research hypothesis examples  

Here are some good research hypothesis examples :  

“The use of a specific type of therapy will lead to a reduction in symptoms of depression in individuals with a history of major depressive disorder.”  

“Providing educational interventions on healthy eating habits will result in weight loss in overweight individuals.”  

“Plants that are exposed to certain types of music will grow taller than those that are not exposed to music.”  

“The use of the plant growth regulator X will lead to an increase in the number of flowers produced by plants.”  

Characteristics that make a research hypothesis weak are unclear variables, unoriginality, being too general or too vague, and being untestable. A weak hypothesis leads to weak research and improper methods.   

Some bad research hypothesis examples (and the reasons why they are “bad”) are as follows:  

“This study will show that treatment X is better than any other treatment . ” (This statement is not testable, too broad, and does not consider other treatments that may be effective.)  

“This study will prove that this type of therapy is effective for all mental disorders . ” (This statement is too broad and not testable as mental disorders are complex and different disorders may respond differently to different types of therapy.)  

“Plants can communicate with each other through telepathy . ” (This statement is not testable and lacks a scientific basis.)  

Importance of testable hypothesis  

If a research hypothesis is not testable, the results will not prove or disprove anything meaningful. The conclusions will be vague at best. A testable hypothesis helps a researcher focus on the study outcome and understand the implication of the question and the different variables involved. A testable hypothesis helps a researcher make precise predictions based on prior research.  

To be considered testable, there must be a way to prove that the hypothesis is true or false; further, the results of the hypothesis must be reproducible.  

Frequently Asked Questions (FAQs) on research hypothesis  

1. What is the difference between research question and research hypothesis ?  

A research question defines the problem and helps outline the study objective(s). It is an open-ended statement that is exploratory or probing in nature. Therefore, it does not make predictions or assumptions. It helps a researcher identify what information to collect. A research hypothesis , however, is a specific, testable prediction about the relationship between variables. Accordingly, it guides the study design and data analysis approach.

2. When to reject null hypothesis ?

A null hypothesis should be rejected when the evidence from a statistical test shows that it is unlikely to be true. This happens when the test statistic (e.g., p -value) is less than the defined significance level (e.g., 0.05). Rejecting the null hypothesis does not necessarily mean that the alternative hypothesis is true; it simply means that the evidence found is not compatible with the null hypothesis.  

3. How can I be sure my hypothesis is testable?  

A testable hypothesis should be specific and measurable, and it should state a clear relationship between variables that can be tested with data. To ensure that your hypothesis is testable, consider the following:  

• Clearly define the key variables in your hypothesis. You should be able to measure and manipulate these variables in a way that allows you to test the hypothesis.  
• The hypothesis should predict a specific outcome or relationship between variables that can be measured or quantified.   
• You should be able to collect the necessary data within the constraints of your study.  
• It should be possible for other researchers to replicate your study, using the same methods and variables.   
• Your hypothesis should be testable by using appropriate statistical analysis techniques, so you can draw conclusions, and make inferences about the population from the sample data.  
• The hypothesis should be able to be disproven or rejected through the collection of data.  

4. How do I revise my research hypothesis if my data does not support it?  

If your data does not support your research hypothesis , you will need to revise it or develop a new one. You should examine your data carefully and identify any patterns or anomalies, re-examine your research question, and/or revisit your theory to look for any alternative explanations for your results. Based on your review of the data, literature, and theories, modify your research hypothesis to better align it with the results you obtained. Use your revised hypothesis to guide your research design and data collection. It is important to remain objective throughout the process.  

5. I am performing exploratory research. Do I need to formulate a research hypothesis?  

As opposed to “confirmatory” research, where a researcher has some idea about the relationship between the variables under investigation, exploratory research (or hypothesis-generating research) looks into a completely new topic about which limited information is available. Therefore, the researcher will not have any prior hypotheses. In such cases, a researcher will need to develop a post-hoc hypothesis. A post-hoc research hypothesis is generated after these results are known.  

6. How is a research hypothesis different from a research question?

A research question is an inquiry about a specific topic or phenomenon, typically expressed as a question. It seeks to explore and understand a particular aspect of the research subject. In contrast, a research hypothesis is a specific statement or prediction that suggests an expected relationship between variables. It is formulated based on existing knowledge or theories and guides the research design and data analysis.

7. Can a research hypothesis change during the research process?

Yes, research hypotheses can change during the research process. As researchers collect and analyze data, new insights and information may emerge that require modification or refinement of the initial hypotheses. This can be due to unexpected findings, limitations in the original hypotheses, or the need to explore additional dimensions of the research topic. Flexibility is crucial in research, allowing for adaptation and adjustment of hypotheses to align with the evolving understanding of the subject matter.

8. How many hypotheses should be included in a research study?

The number of research hypotheses in a research study varies depending on the nature and scope of the research. It is not necessary to have multiple hypotheses in every study. Some studies may have only one primary hypothesis, while others may have several related hypotheses. The number of hypotheses should be determined based on the research objectives, research questions, and the complexity of the research topic. It is important to ensure that the hypotheses are focused, testable, and directly related to the research aims.

9. Can research hypotheses be used in qualitative research?

Yes, research hypotheses can be used in qualitative research, although they are more commonly associated with quantitative research. In qualitative research, hypotheses may be formulated as tentative or exploratory statements that guide the investigation. Instead of testing hypotheses through statistical analysis, qualitative researchers may use the hypotheses to guide data collection and analysis, seeking to uncover patterns, themes, or relationships within the qualitative data. The emphasis in qualitative research is often on generating insights and understanding rather than confirming or rejecting specific research hypotheses through statistical testing.

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Directional vs Non-Directional Hypothesis – Collect Feedback More Effectively 

To conduct a perfect survey, you should know the basics of good research . That’s why in Startquestion we would like to share with you our knowledge about basic terms connected to online surveys and feedback gathering . Knowing the basis you can create surveys and conduct research in more effective ways and thanks to this get meaningful feedback from your customers, employees, and users. That’s enough for the introduction – let’s get to work. This time we will tell you about the hypothesis .

What is a Hypothesis?

A Hypothesis can be described as a theoretical statement built upon some evidence so that it can be tested as if it is true or false. In other words, a hypothesis is a speculation or an idea, based on insufficient evidence that allows it further analysis and experimentation.  

The purpose of a hypothetical statement is to work like a prediction based on studied research and to provide some estimated results before it ha happens in a real position. There can be more than one hypothesis statement involved in a research study, where you need to question and explore different aspects of a proposed research topic. Before putting your research into directional vs non-directional hypotheses, let’s have some basic knowledge.

Most often, a hypothesis describes a relation between two or more variables. It includes:

An Independent variable – One that is controlled by the researcher

Dependent Variable – The variable that the researcher observes in association with the Independent variable.

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How to write an effective Hypothesis?

To write an effective hypothesis follow these essential steps.

• Inquire a Question

The very first step in writing an effective hypothesis is raising a question. Outline the research question very carefully keeping your research purpose in mind. Build it in a precise and targeted way. Here you must be clear about the research question vs hypothesis. A research question is the very beginning point of writing an effective hypothesis.

Do Literature Review

Once you are done with constructing your research question, you can start the literature review. A literature review is a collection of preliminary research studies done on the same or relevant topics. There is a diversified range of literature reviews. The most common ones are academic journals but it is not confined to that. It can be anything including your research, data collection, and observation.

At this point, you can build a conceptual framework. It can be defined as a visual representation of the estimated relationship between two variables subjected to research.

Frame an Answer

After a collection of literature reviews, you can find ways how to answer the question. Expect this stage as a point where you will be able to make a stand upon what you believe might have the exact outcome of your research. You must formulate this answer statement clearly and concisely.

Build a Hypothesis

At this point, you can firmly build your hypothesis. By now, you knew the answer to your question so make a hypothesis that includes:

• Applicable Variables                     
• Particular Group being Studied (Who/What)
• Probable Outcome of the Experiment

Remember, your hypothesis is a calculated assumption, it has to be constructed as a sentence, not a question. This is where research question vs hypothesis starts making sense.

Refine a Hypothesis

Make necessary amendments to the constructed hypothesis keeping in mind that it has to be targeted and provable. Moreover, you might encounter certain circumstances where you will be studying the difference between one or more groups. It can be correlational research. In such instances, you must have to testify the relationships that you believe you will find in the subject variables and through this research.

Build Null Hypothesis

Certain research studies require some statistical investigation to perform a data collection. Whenever applying any scientific method to construct a hypothesis, you must have adequate knowledge of the Null Hypothesis and an Alternative hypothesis.

Null Hypothesis: 

A null Hypothesis denotes that there is no statistical relationship between the subject variables. It is applicable for a single group of variables or two groups of variables. A Null Hypothesis is denoted as an H0. This is the type of hypothesis that the researcher tries to invalidate. Some of the examples of null hypotheses are:

–        Hyperactivity is not associated with eating sugar.

–        All roses have an equal amount of petals.

–        A person’s preference for a dress is not linked to its color.

Alternative Hypothesis: 

An alternative hypothesis is a statement that is simply inverse or opposite of the null hypothesis and denoted as H1. Simply saying, it is an alternative statement for the null hypothesis. The same examples will go this way as an alternative hypothesis:

–        Hyperactivity is associated with eating sugar.

–        All roses do not have an equal amount of petals.

–        A person’s preference for a dress is linked to its color.

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Types of Hypothesis

Apart from null and alternative hypotheses, research hypotheses can be categorized into different types. Let’s have a look at them:

Simple Hypothesis:

This type of hypothesis is used to state a relationship between a particular independent variable and only a dependent variable.

Complex Hypothesis:

A statement that states the relationship between two or more independent variables and two or more dependent variables, is termed a complex hypothesis.

Associative and Causal Hypothesis:

This type of hypothesis involves predicting that there is a point of interdependency between two variables. It says that any kind of change in one variable will cause a change in the other one.  Similarly, a casual hypothesis says that a change in the dependent variable is due to some variations in the independent variable.

Directional vs non-directional hypothesis

Directional hypothesis:.

A hypothesis that is built upon a certain directional relationship between two variables and constructed upon an already existing theory, is called a directional hypothesis. To understand more about what is directional hypothesis here is an example, Girls perform better than boys (‘better than’ shows the direction predicted)

Non-directional Hypothesis:

It involves an open-ended non-directional hypothesis that predicts that the independent variable will influence the dependent variable; however, the nature or direction of a relationship between two subject variables is not defined or clear.

For Example, there will be a difference in the performance of girls & boys (Not defining what kind of difference)

As a professional, we suggest you apply a non-directional alternative hypothesis when you are not sure of the direction of the relationship. Maybe you’re observing potential gender differences on some psychological test, but you don’t know whether men or women would have the higher ratio. Normally, this would say that you are lacking practical knowledge about the proposed variables. A directional test should be more common for tests. 

Author: Ula Kamburov-Niepewna

Updated: 18 November 2022

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• Null and Alternative Hypotheses | Definitions & Examples

Null & Alternative Hypotheses | Definitions, Templates & Examples

Published on May 6, 2022 by Shaun Turney . Revised on June 22, 2023.

The null and alternative hypotheses are two competing claims that researchers weigh evidence for and against using a statistical test :

• Null hypothesis ( H 0 ): There’s no effect in the population .
• Alternative hypothesis ( H a or H 1 ) : There’s an effect in the population.

Table of contents

Answering your research question with hypotheses, what is a null hypothesis, what is an alternative hypothesis, similarities and differences between null and alternative hypotheses, how to write null and alternative hypotheses, other interesting articles, frequently asked questions.

The null and alternative hypotheses offer competing answers to your research question . When the research question asks “Does the independent variable affect the dependent variable?”:

• The null hypothesis ( H 0 ) answers “No, there’s no effect in the population.”
• The alternative hypothesis ( H a ) answers “Yes, there is an effect in the population.”

The null and alternative are always claims about the population. That’s because the goal of hypothesis testing is to make inferences about a population based on a sample . Often, we infer whether there’s an effect in the population by looking at differences between groups or relationships between variables in the sample. It’s critical for your research to write strong hypotheses .

You can use a statistical test to decide whether the evidence favors the null or alternative hypothesis. Each type of statistical test comes with a specific way of phrasing the null and alternative hypothesis. However, the hypotheses can also be phrased in a general way that applies to any test.

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The null hypothesis is the claim that there’s no effect in the population.

If the sample provides enough evidence against the claim that there’s no effect in the population ( p ≤ α), then we can reject the null hypothesis . Otherwise, we fail to reject the null hypothesis.

Although “fail to reject” may sound awkward, it’s the only wording that statisticians accept . Be careful not to say you “prove” or “accept” the null hypothesis.

Null hypotheses often include phrases such as “no effect,” “no difference,” or “no relationship.” When written in mathematical terms, they always include an equality (usually =, but sometimes ≥ or ≤).

You can never know with complete certainty whether there is an effect in the population. Some percentage of the time, your inference about the population will be incorrect. When you incorrectly reject the null hypothesis, it’s called a type I error . When you incorrectly fail to reject it, it’s a type II error.

Examples of null hypotheses

The table below gives examples of research questions and null hypotheses. There’s always more than one way to answer a research question, but these null hypotheses can help you get started.

*Note that some researchers prefer to always write the null hypothesis in terms of “no effect” and “=”. It would be fine to say that daily meditation has no effect on the incidence of depression and p 1 = p 2 .

The alternative hypothesis ( H a ) is the other answer to your research question . It claims that there’s an effect in the population.

Often, your alternative hypothesis is the same as your research hypothesis. In other words, it’s the claim that you expect or hope will be true.

The alternative hypothesis is the complement to the null hypothesis. Null and alternative hypotheses are exhaustive, meaning that together they cover every possible outcome. They are also mutually exclusive, meaning that only one can be true at a time.

Alternative hypotheses often include phrases such as “an effect,” “a difference,” or “a relationship.” When alternative hypotheses are written in mathematical terms, they always include an inequality (usually ≠, but sometimes < or >). As with null hypotheses, there are many acceptable ways to phrase an alternative hypothesis.

Examples of alternative hypotheses

The table below gives examples of research questions and alternative hypotheses to help you get started with formulating your own.

Null and alternative hypotheses are similar in some ways:

• They’re both answers to the research question.
• They both make claims about the population.
• They’re both evaluated by statistical tests.

However, there are important differences between the two types of hypotheses, summarized in the following table.

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To help you write your hypotheses, you can use the template sentences below. If you know which statistical test you’re going to use, you can use the test-specific template sentences. Otherwise, you can use the general template sentences.

General template sentences

The only thing you need to know to use these general template sentences are your dependent and independent variables. To write your research question, null hypothesis, and alternative hypothesis, fill in the following sentences with your variables:

Does independent variable affect dependent variable ?

• Null hypothesis ( H 0 ): Independent variable does not affect dependent variable.
• Alternative hypothesis ( H a ): Independent variable affects dependent variable.

Test-specific template sentences

Once you know the statistical test you’ll be using, you can write your hypotheses in a more precise and mathematical way specific to the test you chose. The table below provides template sentences for common statistical tests.

Note: The template sentences above assume that you’re performing one-tailed tests . One-tailed tests are appropriate for most studies.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Descriptive statistics
• Measures of central tendency
• Correlation coefficient

Methodology

• Cluster sampling
• Stratified sampling
• Types of interviews
• Cohort study
• Thematic analysis

Research bias

• Implicit bias
• Cognitive bias
• Survivorship bias
• Availability heuristic
• Nonresponse bias
• Regression to the mean

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

Null and alternative hypotheses are used in statistical hypothesis testing . The null hypothesis of a test always predicts no effect or no relationship between variables, while the alternative hypothesis states your research prediction of an effect or relationship.

The null hypothesis is often abbreviated as H 0 . When the null hypothesis is written using mathematical symbols, it always includes an equality symbol (usually =, but sometimes ≥ or ≤).

The alternative hypothesis is often abbreviated as H a or H 1 . When the alternative hypothesis is written using mathematical symbols, it always includes an inequality symbol (usually ≠, but sometimes < or >).

A research hypothesis is your proposed answer to your research question. The research hypothesis usually includes an explanation (“ x affects y because …”).

A statistical hypothesis, on the other hand, is a mathematical statement about a population parameter. Statistical hypotheses always come in pairs: the null and alternative hypotheses . In a well-designed study , the statistical hypotheses correspond logically to the research hypothesis.

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• Open access
• Published: 02 September 2024

Individual bacterial cells can use spatial sensing of chemical gradients to direct chemotaxis on surfaces

• James H. R. Wheeler   ORCID: orcid.org/0009-0006-8677-5086 1 , 2 , 3 ,
• Kevin R. Foster   ORCID: orcid.org/0000-0003-4687-6633 2 , 3 &
• William M. Durham   ORCID: orcid.org/0000-0002-8827-4705 1 , 2  

Nature Microbiology volume  9 ,  pages 2308–2322 ( 2024 ) Cite this article

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• Bacterial physiology
• Cellular microbiology
• Cellular motility

Swimming bacteria navigate chemical gradients using temporal sensing to detect changes in concentration over time. Here we show that surface-attached bacteria use a fundamentally different mode of sensing during chemotaxis. We combined microfluidic experiments, massively parallel cell tracking and fluorescent reporters to study how Pseudomonas aeruginosa senses chemical gradients during pili-based ‘twitching’ chemotaxis on surfaces. Unlike swimming cells, we found that temporal changes in concentration did not induce motility changes in twitching cells. We then quantified the chemotactic behaviour of stationary cells by following changes in the sub-cellular localization of fluorescent proteins as cells are exposed to a gradient that alternates direction. These experiments revealed that P. aeruginosa cells can directly sense differences in concentration across the lengths of their bodies, even in the presence of strong temporal fluctuations. Our work thus overturns the widely held notion that bacterial cells are too small to directly sense chemical gradients in space.

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Cellular chemotaxis, the ability to sense chemical gradients and actively direct motility along them, plays a central role in many important processes including disease 1 , 2 , foraging 3 , 4 , sexual reproduction 5 and multicellular development 6 , 7 . There are two distinct ways that cells can sense chemical gradients (Fig. 1 ). Cells using temporal sensing measure changes in chemical concentration over time as they travel along gradients. By contrast, cells using spatial sensing directly compare the concentration of a chemical at different positions along their cell body, independently from cell movement. The two sensing mechanisms are not necessarily mutually exclusive; in some complex signal transduction systems (for example, in certain eukaryotic cells that travel along surfaces using amoeboid movement), they can also be used in combination to guide chemotaxis 8 .

In principle, chemotaxing cells could either sense changes in chemoattractant concentration by moving from one location to another and comparing how the concentration changes over time (temporal sensing) or by directly comparing differences in concentration over the length of their bodies (spatial sensing). The rapid speed of swimming bacteria (for example, ref. 39 ) means that over the course of their typical response time (on the order of 1 s), they would experience a larger change in concentration in time than space (denoted by the green and purple bars, respectively). The opposite is true for solitary surface-attached twitching bacteria, which move much more slowly (Extended Data Fig. 1c ) and have response times on the order of 1 min 33 . Here chemoreceptor clusters are represented by the grey circles within the cell poles.

While eukaryotic cells are capable of both forms of sensing, the paradigm in the study of bacterial chemotaxis is one of temporal sensing. In particular, whenever the chemosensory systems of swimming bacteria have been characterized in detail, they have exclusively been found to use temporal sensing mechanisms to detect chemical gradients 9 , 10 , 11 , 12 , 13 , 14 , 15 , and these are particularly well understood in swimming Escherichia coli 16 , 17 , 18 , 19 , 20 . Temporal sensing allows fast-swimming bacteria to measure changes in concentration that occur over length scales equivalent to tens of cell body lengths (Fig. 1 and Supplementary Information ), enabling them to better distinguish chemical gradients from stochastic noise. However, the advantage conferred by temporal sensing is predicted to scale with movement speed, and theoretical models suggest that spatial sensing could potentially confer increased sensitivity to bacteria-sized swimming cells in some parameter regimes 21 , 22 . Despite this, there is only one potential observation of spatial sensing in bacteria, which was suggested as an explanation for the U-shaped trajectories made by an uncultured bacterium collected from marine sediments that swims using flagella extending from each of its two poles 23 . However, these analyses were not definitive, and swimming bacteria are generally understood to use temporal sensing to guide chemotaxis 20 , 24 , 25 , 26 , 27 , 28 , 29 .

This focus on swimming cells contrasts with the fact that most bacteria live in surface-attached communities called biofilms 30 , 31 , 32 . Flagella are ineffective at driving motility in surface-attached cells 33 , 34 , 35 ; instead they propel themselves using other forms of motility 36 , 37 . For instance, many surface-attached bacteria move via twitching motility, which is driven by the extension and retraction of type IV pili that function like molecular grappling hooks to pull cells across surfaces 38 . It was previously demonstrated that individual Pseudomonas aeruginosa cells can use twitching motility to navigate chemoattractant gradients 33 . Specifically, when exposed to a chemoattractant gradient that alternated direction, surface-attached cells were observed to rapidly reverse direction in response, typically before travelling a single micron. In contrast to swimming cells that reverse direction by switching the direction of flagellar rotation 39 , twitching cells reverse direction by switching pili activity to the opposite pole of their rod-shaped bodies 40 , 41 . However, it is not known how surface-attached P. aeruginosa cells resolve which of their poles is directed toward higher chemoattractant concentrations as they navigate chemical gradients.

A priori, there are good reasons to suspect that surface-attached P. aeruginosa cells might use a different type of gradient sensing compared to swimming cells (Fig. 1 and Supplementary Information ). On average, solitary twitching cells migrate approximately four orders of magnitude more slowly than swimming cells 33 , 39 . Whereas swimming bacteria typically cover a distance equivalent to tens of body lengths within the characteristic time it takes for them to respond to chemoattractant gradients (~1 s; ref. 42 ), twitching P. aeruginosa cells typically only move less than one fifth of their cell body length in their characteristic response time (~1 min; ref. 33 ). Swimming cells would thus detect a larger change in concentration by sensing temporal changes as they move, whereas the opposite is true for twitching cells, which could measure a larger change in concentration across the length of their bodies (Fig. 1 ). While surface-attached bacteria are known to detect non-chemical stimuli, such as light and mechanical forces (both of which are intrinsically vectorial), over the lengths of their bodies 41 , 43 , we currently do not know whether they are also capable of sensing chemical concentration (which is a scalar) in analogous fashion. We therefore decided to investigate whether surface-attached P. aeruginosa cells, like eukaryotes, can detect chemical gradients across their cell bodies. To accomplish this, we used a series of microfluidic experiments to measure the response of individual solitary bacteria as they were exposed to different types of chemical stimulus.

Twitching cells do not respond to temporal gradients

While one can argue how spatial sensing might benefit twitching P. aeruginosa cells (Fig. 1 and Supplementary Information ), it is well documented that bacteria use temporal mechanisms when swimming. We therefore began by testing whether temporal changes in chemoattractant concentration could explain the directed motility of P. aeruginosa on surfaces. The experiments that documented pili-based chemotaxis used a dual-flow microfluidic device where molecular diffusion mixes two streams of fluid with different chemoattractant concentrations as they flow down the length of the device (Extended Data Fig. 1 and ref. 33 ). In these assays, cells undergoing chemotaxis simultaneously experience a spatial gradient over the length of their bodies as well as temporal changes in chemoattractant concentration as they move along the gradient. This makes it difficult to ascertain whether cells are responding to either spatial or temporal stimuli.

To directly test whether twitching cells use temporal signals to guide chemotaxis, we developed a custom microfluidic set-up that uses Taylor–Aris dispersion 44 , 45 to generate a concentration gradient of succinate (a known chemoattractant and preferred carbon source of P. aeruginosa ; Extended Data Fig. 1 and ref. 33 ) that flows past cells. Importantly, our custom microfluidic set-up exposes all cells to an approximately equal temporal stimulus, independent of their movement speed or direction (Fig. 2 and Methods ). Twitching cells in dual-flow microfluidic experiments bias their motility towards succinate by both increasing and decreasing their reversal frequency when moving away from or towards chemoattractants, respectively, compared to a control that contains a uniform concentration of succinate (Extended Data Fig. 1 ). Therefore, if cells indeed used temporal measurements to guide chemotaxis, we would expect that a temporal decrease in succinate concentration would cause the cells in our Taylor–Aris dispersion experiments to reverse more frequently, and vice versa.

a , We used Taylor–Aris dispersion to generate concentration gradients along a 2 m long tube, which then flowed past surface-attached cells in microfluidic devices. We filled the system with media containing succinate ( C MAX  = 1.16 mM). At t  = 0, media containing a lower succinate concentration ( C MIN  = 0.84 mM) was pulled through the system. As fluid moves fastest along a tube’s centreline, a plug of lower concentration fluid forms (panel i) but is rapidly mixed across the tube width via molecular diffusion (panel ii). The fluid interface forms a longitudinal gradient with an ~1.6 m length scale such that surface-attached cells within the device experience smooth temporal decreases in concentration (panel iii to panel iv). b , c , Using dye, we quantify succinate concentration ( b ) and temporal concentration gradient over time ( c ) (blue lines; dashed green lines show a control with 1 mM succinate throughout). Cells experience approximately the same mean temporal concentration gradient that cells experience in dual-inlet chemotaxis experiments (Extended Data Fig. 1 and ref. 33 ), but with ~16,000-fold smaller spatial gradients. d , In the 1 h period before the succinate gradient entered the device (interval t 1 ), cell reversal rates were statistically indistinguishable between experiment (white bar, blue outline) and control (white bar, green outline; one-sided exact Poisson test (Methods) yielded P  = 0.316). Similarly, reversal rates in the presence of a temporal succinate gradient (interval t 2 ; light grey bar, blue outline) and in the 1 h period after the gradient had cleared the microfluidic device (interval t 3 ; dark grey bar, blue outline) were statistically indistinguishable from the reversal rates in the control ( P  = 0.842 and P  = 0.368). The number of reversals observed was n r  = 1,496 and 1,391 across n t  = 468,596 and 439,632 trajectory points in the control and experimental conditions, respectively. Error bars show 95% confidence intervals about the mean reversal rates assuming that reversals follow a Poisson distribution (Methods). Data shown here are representative of two bio-replicates (Extended Data Fig. 5 ). Source data provided as a Source data file.

Source data

We designed our Taylor–Aris dispersion experiments to expose cells to the same average chemical temporal stimuli that cells experienced in the dual-flow experiments where chemotaxis was originally demonstrated. This correspondence was accomplished by matching both the concentrations ( C ) and mean temporal concentration gradients (d C /d t ) that cells experience in those experiments (Methods). Importantly, in our Taylor–Aris dispersion experiments, the chemoattractant gradient forms over the length of a 2-m-long tube leading to the microfluidic device (Fig. 2a ), such that the chemical gradient measures approximately 1.6 m in length by the time it reaches the cells. By contrast, in dual-flow experiments, the gradient instead forms across the width of the microfluidic device and has a characteristic length scale of 100 µm. Therefore, the cells in our Taylor–Aris dispersion experiments experience approximately a 16,000-fold smaller gradient across the length of their bodies (that is, d C /d x ) compared to the dual-inlet experiments, while experiencing approximately the same mean temporal stimuli (d C /d t ).

We used massively parallel cell tracking and automated reversal detection 33 to simultaneously quantify the movement of thousands of cells attached to the surface of a microfluidic device (Extended Data Fig. 2 ). In addition to exposing cells to temporal gradients of succinate, we ran a control experiment in an adjacent microfluidic channel on the same microscope where cells were exposed to a constant succinate concentration over time. This control allows us to distinguish any potential changes in cell motility induced by the temporal succinate gradient from other, more general changes in cell motility over time. For example, an increase in the amount of exopolysaccharides present on the surface 46 or physiological adaptation of cells to the surface (mediated, for example, by surface sensing and second messengers such as cyclic adenosine monophosphate 47 , 48 , 49 ) could change cell motility over time (Extended Data Figs. 2 and 3 ). To control for such effects, we established a baseline by analysing cell motility in the 1 h period that preceded the succinate gradient entering the microfluidic device (white region labelled t 1 in Fig. 2b,c ) and compared it to that measured over the same time period in the control. As reversals are relatively rare events 33 , we imaged six fields of view in each channel, which allowed us to track approximately 10 4 cells simultaneously (Extended Data Fig. 2 ). We found that the baseline reversal rate before the gradient entered the microfluidic channel (white region labelled t 1 in Fig. 2b ) was statistically indistinguishable when compared with the reversal rate observed in the control over the same time period (Fig. 2d and Extended Data Figs. 4 and 5 ). This strong correspondence thus indicates that we can directly compare the cellular reversal rates in the two channels at later time points to assess whether a temporal gradient in concentration causes cells to alter their reversal rate.

We next calculated the reversal rate of cells as they experienced a temporal decrease or increase in succinate concentration (light grey region labelled t 2 in Fig. 2b,c ) and compared it to that measured over the same time period in the constant succinate concentration control. Regardless of whether cells were exposed to a temporal increase or decrease in succinate concentration, cell reversal rates in time period t 2 were statistically indistinguishable when compared between experimental and control conditions (Fig. 2d and Extended Data Figs. 4 and 5 ). Finally, we measured reversal rates in the 1 h time period after the temporal gradient had cleared the microfluidic device to confirm that the gradients did not have a latent effect on cell reversal rates (dark grey region labelled t 3 in Fig. 2b,c ). Once again, cell reversal rates in time period t 3 were statistically indistinguishable when comparing between the control and experimental conditions (Fig. 2d and Extended Data Figs. 4 and 5 ). Taken together, our results thus strongly suggest that cells do not alter their reversal rate in response to temporal succinate gradients. While it is known that twitching cells generate chemotaxis by actively modulating their reversal frequency in response to the direction that they are travelling along a chemoattractant gradient (Extended Data Fig. 1 ; ref. 33 ), the absence of a response in our Taylor–Aris dispersion experiments suggests that P. aeruginosa cells do not use the mean temporal changes in concentration they experience to guide pili-based chemotaxis.

However, we decided to explore another possible basis for temporal sensing. While the Taylor–Aris dispersion experiments simulated the long-term, average temporal changes in concentration experienced by cells in experiments where chemotaxis was observed, on shorter timescales, twitching cells routinely undergo much more rapid movement caused by the stochastic release of individual pili 38 , 50 . These rapid movements can momentarily transport cells at speeds that are approximately 20-fold larger than their movement speeds during their more regular, slower form of movement, and thus they could expose cells to larger temporal stimuli (Supplementary Information ). This is because the magnitude of the temporal gradient a cell experiences scales with cell velocity, V C , relative to a chemical gradient like d C /d t  =  V C d C /d x . Therefore, to measure the response of twitching cells to more rapid changes in succinate concentration, we used a programmable microfluidic system that smoothly switches between two different concentrations of succinate over a period of 1.5 min, yielding temporal gradients, d C /d t , that are approximately 40-fold larger than the experiments shown in Fig. 2c ( Methods ). Given the short timescale of these temporal gradients, we alternated between two different succinate concentrations >12 times over the course of each experiment, allowing us to expose the same cells to both positive and negative temporal concentration gradients and analyse data across them separately. While these temporal gradients were much sharper than those in the Taylor–Aris dispersion experiments, we again found that temporal stimuli did not generate any detectable changes in cell reversal rates (Extended Data Fig. 6 ). Taken together, these first experiments strongly suggest that surface-attached P. aeruginosa do not use temporal stimuli to determine whether they are moving up or down a chemical gradient.

Quantifying chemotactic behaviour in stationary cells

Our first experiments indicated that twitching chemotaxis is not driven by temporal sensing, suggesting instead that P. aeruginosa cells might directly sense differences in concentration across the length of their bodies. However, to evaluate this possibility, we needed to find a way to experimentally decouple the spatial and temporal information that cells experience. The challenge is that a cell moving through a steady spatial gradient of chemoattractant will experience differences in concentration along the length of its body, while simultaneously experiencing changes in concentration over time as it moves relative to the gradient. To decouple these two different stimuli from one another, we decided to study the behaviour of stationary cells, which typically make up a relatively small percentage of cells within our microfluidic assays (approximately 5–10%). The question then was how does one characterize chemotactic behaviour in cells that are not moving?

Here, we initially found inspiration in the studies of Myxococcus xanthus , which can also move via twitching motility 51 . Reversals occur 40 times more frequently in M. xanthus and are accompanied by changes in the sub-cellular localization of two motor proteins, PilB and PilT, which are responsible for pili extension and retraction, respectively 52 , 53 , 54 . In twitching M. xanthus cells, PilB localizes to the front pole of a moving cell (the ‘leading pole’), whereas PilT localizes predominantly to the rear pole (the ‘trailing pole’). The two motor proteins then switch between the two poles of M. xanthus cells during reversals. If these motor proteins show similar patterns of localization in twitching P. aeruginosa cells, we could potentially use fluorescent fusions to quantify reversals in cell polarity, even in cells that are temporarily stationary.

To visualize the retraction motor PilT in cells undergoing reversals, we fused PilT to yellow fluorescent protein (YFP) and expressed it in a P. aeruginosa strain lacking a functional native copy of PilT ( ΔpilT :: pilT-yfp ; Methods and Table 1 ). This fusion protein complemented the motility of the ΔpilT strain (Extended Data Fig. 7 ), a mutant lacking the first portion of the gene’s coding region (Methods; ref. 55 ). We find that our PilT–YFP fusion protein localizes predominantly to the leading cell pole in twitching P. aeruginosa cells (Fig. 3b,c ), which is consistent with two recent studies 41 , 56 , and implies that reversals in cell movement direction will be associated with a re-localization of PilT–YFP to a cell’s new leading pole (for example, Fig. 3d ). Given that PilT instead localizes to the trailing pole in twitching M. xanthus cells, this implies that different molecular mechanisms are used to generate reversals in these two species.

a , b , In the majority of both stationary ( a ) and motile ( b ) cells, the PilT–YFP fusion protein localizes to one of the two cell poles (unipolar). A smaller proportion of cells have PilT–YFP localizations in both poles (bipolar) or lack appreciable localizations altogether (nonpolar). Black lines show the mean of three bio-replicates that were each conducted on different days, represented here with a different coloured circle. The data from each bio-replicate contained over n  = 1,000 trajectories. c , If we consider only those motile cells that have a unipolar PilT–YFP localization, we find that PilT–YFP is significantly more likely to localize to a cell’s leading pole (mean proportion = 0.84; a two-sided binomial test of proportions rejects the null hypothesis of equal proportions with P  < 1 × 10 −10 for each bio-replicate, assuming that data from each cell at each time point are independent measurements). d , A time series of a motile twitching cell (cell outline shown in blue) undergoing a reversal at t  = 8 min. PilT–YFP (shown in white) localizes to the leading pole, so that it swaps from one pole to the other when the cell reverses direction. e , A time series of a stationary cell reveals that PilT–YFP can swap between a cell’s two poles over time, an event we call a ‘repolarization event’. Localizations of PilT–YFP are marked with white triangles. f , A cell that is initially stationary has PilT–YFP localized to both of its poles, but subsequently PilT–YFP accumulates within its bottom pole shortly before the cell initiates movement in the downward direction. Faint dashed red lines in e and f mark the position of the two cell poles in the first image of the time series. g – i , Repolarization events can occur in cells that are initially nonpolar ( g ), unipolar ( h ) or bipolar ( i ). Cells shown are representative of three bio-replicates. Source data provided as a source data file.

In stationary cells, PilT–YFP can also localize to neither (‘nonpolar’), to one (‘unipolar’) or to both cell poles simultaneously (‘bipolar’; Fig. 3a ). Crucially, we found that the localization of PilT–YFP remains dynamic in the stationary cells in our microfluidic assays, with new localizations forming and old localizations dissipating over time (Fig. 3e,f ). These findings indicate that changes in the sub-cellular localization of PilT–YFP can be used to distinguish between the leading and lagging pole before a cell starts to move. Specifically, this fusion allows us to detect ‘repolarization events’ in stationary cells, which occur when PilT–YFP redistributes within the cell (Fig. 3g–i ), and quantify how they are elicited by different types of chemical gradients. Tracking changes in the sub-cellular localization of PilT–YFP therefore allows us to analyse the chemotactic behaviour of stationary cells.

Spatial sensing guides twitching chemotaxis

To test for spatial sensing, we used a custom Y-shaped microfluidic device 33 to expose our P. aeruginosa ( ΔpilT :: pilT-yfp ) cells to a spatial gradient of succinate that alternates in direction (Fig. 4a ). We then followed the distribution of PilT–YFP within a total of >1,000 stationary cells and recorded whether or not these stationary cells underwent repolarization events when they were exposed to a succinate gradient that alternated direction approximately every 45–60 min (Methods). Stationary cells that underwent repolarization events can be separated into two different categories: ‘correct’ repolarization events in which cells re-localize PilT–YFP in the pole experiencing higher succinate concentrations and ‘incorrect’ repolarization events, where PilT–YFP is re-localized in the pole experiencing lower succinate concentrations (Fig. 4b ).

a , We used a dual-flow microfluidic device to expose cells to a spatial gradient of succinate that alternates direction 33 . The dashed black box indicates the region downstream of the two inlets, where we imaged cells. b , In response to this alternating spatial gradient, stationary cells (blue) expressing PilT–YFP (white circles) can undergo either correct or incorrect repolarization events. c , d , The relative proportion of correct and incorrect repolarization events in this experiment can be used to determine whether cells use temporal ( c ) or spatial ( d ) sensing. c , Stationary cells using only temporal sensing could garner no information about a gradient’s spatial orientation and would therefore be equally likely to generate correct and incorrect repolarization events (prediction A). d , By contrast, stationary cells capable of spatial sensing could directly sense the gradient’s spatial orientation, allowing them to deploy correct repolarization events at a greater frequency than incorrect repolarization events (prediction B). e , Quantifying the behaviour of n  = 171 stationary cells undergoing repolarization events within our alternating gradient experiments (see Supplementary Videos 1 – 16 and Supplementary Table 1 ) revealed that correct repolarization events occurred approximately 6 times more frequently than incorrect repolarization events, regardless of whether PilT–YFP localization was initially nonpolar, unipolar or bipolar (Fig. 3g–i ). An exact two-tailed binomial test rejected the null hypothesis that correct and incorrect repolarization events were equally abundant with P  = 2.37 × 10 −7 , 1.51 × 10 −9 and 1.28 × 10 −8 for nonpolar, unipolar and bipolar repolarization events, respectively. This is consistent with prediction B, indicating that cells are capable of directly sensing differences in concentration over the length of their bodies. Error bars show 95% confidence intervals about the proportion estimates. Source data provided as a Source data file.

The relative frequency of correct and incorrect repolarization events in stationary cells allows us to directly test whether cells respond to temporal or spatial stimuli. As stationary cells do not move appreciably relative to the gradient, the temporal stimuli they experience do not encode information that could allow them to determine the orientation of the chemical gradient. Instead, on one side of the device stationary cells simply experience an increase in concentration over time, whereas on the other side, they experience a decrease in concentration over time (Fig. 4c,d ). Therefore, temporal sensing and spatial sensing lead to two different, and easily distinguishable, predictions in these experiments. If stationary cells used temporal sensing, repolarization events would be independent of the gradient’s orientation, so one would expect that correct and incorrect repolarization events would both occur randomly and, therefore, at approximately the same rate (‘prediction A’; Fig. 4c ). By contrast, if stationary cells can make spatial measurements, we expect that correct repolarization events will occur more often than incorrect repolarization events. This is because cells that sense the direction of the chemical gradient by directly measuring it across their bodies would be able to correctly ascertain the gradient’s spatial orientation (‘prediction B’; Fig. 4d ).

Across three bio-replicates, we identified 171 cells that were stationary following the change in gradient orientation and subsequently underwent repolarization events (Fig. 4e , Supplementary Videos 1 – 16 and Supplementary Table 1 ; a detailed description of how repolarization events were identified is given in the Methods ). A fraction of stationary cells sometimes began to move off after the gradient changed direction before observably altering their PilT–YFP distribution, so we also used cell movement to diagnose the chemotactic response of these initially stationary cells (Methods). Separating these 171 repolarization events by direction revealed a striking result: correct repolarization events occurred approximately 6 times more frequently than incorrect ones (148 correct, 23 incorrect; Fig. 4e ), suggesting therefore that twitching cells directly sense chemoattractant gradients across the length of their cell bodies. This trend is remarkably consistent across stationary cells regardless of whether their initial PilT–YFP localization is nonpolar, unipolar or bipolar (Fig. 4e ). Moreover, cells were observed to correctly determine the direction of the succinate gradient despite being subjected to sharp changes in succinate concentration over time (Fig. 5a–c , Extended Data Fig. 8 , Supplementary Videos 1 – 16 and Supplementary Table 1 ). These temporal changes in concentration were two to three orders of magnitude larger than those in the Taylor–Aris dispersion experiments, indicating that spatial sensing is robust to large temporal changes in concentration, such as the random fluctuations that arise from twitching cell’s jerky movement relative to a chemical gradient. Last, we note that twitching P. aeruginosa cells always show a basal level of reversals even in the absence of chemical gradients 33 , which means that a proportion of incorrect repolarization events are expected, albeit at a lower frequency than correct ones (Figs. 4d,e and 5d ).

We simultaneously quantified the succinate concentration that a cell experienced over time (red circles; black line shows moving average), cell speed (grey line) and PilT–YFP localization, as cells were exposed to a succinate gradient that alternates direction. Grey circles indicate time points at which cell images are shown (at 2.5 min intervals). To guide the eye, cell images have been repositioned so that they are vertically oriented and their centroid remains at a fixed position. a , This cell experiences a sharp temporal decrease in succinate concentration when the gradient changes direction. PilT–YFP re-localizes to the cell pole that is now exposed to higher chemoattractant concentrations (a correct repolarization event), and the cell later moves off in the direction of its new leading pole. PilT–YFP is shown in the bottom inset, with red and white boxes indicating high and low succinate concentrations, respectively. b , This cell experiences a sharp increase in succinate concentration over time and also undergoes a correct repolarization event. While PilT–YFP is initially nonpolar, it subsequently re-localizes exclusively to the cell pole positioned in higher succinate concentrations. c , This cell was positioned close to the centreline of the succinate gradient such that when the gradient alternated direction, it experienced noisy fluctuations in succinate concentration, including both increases and decreases in concentration. Despite this, the cell also underwent a correct repolarization event—PilT–YFP was initially localized to both poles (with no observable directional polarity) and subsequently re-localized exclusively to the cell pole positioned in higher succinate concentrations. d , Although less frequent, cells also underwent incorrect repolarization events. Here a cell experiencing an increase in succinate concentration over time re-localizes PilT–YFP to the cell pole positioned in lower succinate concentrations and subsequently moves in that direction. While these four repolarization events are representative, Supplementary Videos 1 – 16 show every repolarization event that we observed, with a description of how each was classified in Supplementary Table 1 . Source data provided as a source data file.

The temporal changes did produce interesting trends, however. We observed more repolarization events in cells that experienced a sudden decrease in succinate concentration compared to those experiencing an increase in concentration (Extended Data Fig. 8 ). These findings are broadly consistent with previous work showing that the likelihood of responding to a stimulus increases when the background levels of that stimulus are lower (for example, a prediction of Weber’s law or receptor saturation kinetics 57 , 58 , 59 ). In our alternating gradient experiments, we observed that more cells responded to the new gradient direction when they were experiencing a lower absolute concentration of succinate. We observed a similar pattern in our standard chemotaxis assays; that is, for a given gradient strength, cells are more likely to undergo correct reversals (and less likely to undergo incorrect reversals) when the absolute concentration of succinate was lower (Extended Data Fig. 9 ). However, while background concentration influences the response, we still found that correct repolarization events outnumbered incorrect repolarization events regardless of the background concentration that cells were exposed to. Specifically, in the alternating gradient experiments, correct reversals outnumbered incorrect ones by approximately tenfold when the concentration was decreasing, whereas an approximately fourfold difference was observed when the concentration was increasing (Extended Data Fig. 8 ). These results suggest that cells can correctly identify the direction of the spatial gradient across the lengths of their bodies across a range of absolute concentrations and regardless of the sign of the temporal gradient. Taken together, our data suggest that P. aeruginosa cells can robustly navigate chemoattractant gradients using spatial sensing.

We find that surface-attached P. aeruginosa cells can directly measure differences in concentration over the length of their bodies. By contrast, the signal transduction systems that guide chemotaxis in diverse swimming bacteria, including P. aeruginosa , use temporal sensing 20 , 24 , 29 . The use of spatial sensing was previously thought to be confined to the sophisticated signal transduction systems of eukaryotic cells 8 , 25 . Eukaryotic spatial sensing is regulated by a molecular ‘compass’ composed of intracellular chemical gradients. These gradients are generated from competition between rapid excitatory signalling generated by chemoeffector–chemoreceptor binding and slower, cell-wide inhibitory signalling, known as localized excitation, global inhibition or LEGI interactions 60 , 61 . It has recently been demonstrated that twitching P. aeruginosa cells are able to sense differences in mechanical stimuli across the lengths of their bodies via the two response regulators of the Pil-Chp chemotaxis-like system (PilG and PilH, which may prove comparable to the eukaryotic-like LEGI system 41 ). We find here that PilG is also required for twitching chemotaxis towards succinate (Extended Data Fig. 10 ), and it is therefore possible that similar LEGI interactions could facilitate spatial measurements of chemical gradients in P. aeruginosa . We also note that the putative chemoreceptor of the Pil-Chp chemosensory system (PilJ) localizes to both cell poles in P. aeruginosa 62 , which could potentially facilitate spatial measurements.

Bacteria commonly live on surfaces, where they often experience strong and stable chemical gradients generated by a combination of molecular diffusion, nutrient consumption and the secretion of compounds from both nearby groups of bacteria and other organisms 63 , 64 , 65 , 66 , 67 , 68 . For example, it has recently been demonstrated that P. aeruginosa cells use pili to navigate towards compounds produced by nearby Staphylococcus aureus microcolonies and subsequently inhibit S. aureus growth 69 . Our results show that the well-established paradigm of bacterial chemotaxis, based on measuring changes in concentration over time, does not hold for surface-based movement in P. aeruginosa . Instead, we find that cells navigate on surfaces using spatial information. This mode of sensing is well suited to the slow movement and steep chemical gradients associated with living on surfaces and, relative to temporal sensing, it likely would allow twitching cells to measure larger changes in concentration, enhancing their ability to discriminate chemical gradients from stochastic noise (Fig. 1 , Supplementary Information and refs. 21 , 27 , 70 ). Indeed, our experiments show that even stationary cells can use spatial information to sense chemical gradients. This observation raises the possibility that static bacteria living in mature biofilms could use the multiple, opposing chemical gradients that often form within biofilms 71 to guide biofilm development.

Bacterial strains and culturing

Wild-type P. aeruginosa PAO1 (Kolter collection, ZK2019) was used as the model organism for this study. To visualize the localization of PilT within cells, we sought to express a fluorescently labelled copy of PilT from the native promoter of pilT on the chromosome. However, we were not able to detect any fusion protein using this approach with epi-fluorescent imaging, presumably because the native expression levels of pilT were too low. We therefore sought an alternative solution. First, we generated a pilT mutant lacking the first portion of the gene’s coding region in our model PAO1 strain using a previously published plasmid kindly gifted to us for this study (pJB203 55 ; we refer to this mutant as ΔpilT ). We then generated a PilT–YFP protein fusion expressed from a low-expression promoter (BG35) previously characterized in Pseudomonas putida 72 . Briefly, pilT was amplified from the chromosome of PAO1 using two primers that were complementary to the sequence immediately downstream of the pilT start codon (PILT_F) and ~100 base pairs downstream of the pilT stop codon (PILT_R; see Table 1 for primer sequences). The coding sequence of YFP was amplified from the plasmid pEYFP-N1 (Clontech) using an upstream primer (YFP_F) that additionally introduced the BG35 promoter immediately upstream of a ribosome binding site (designed using automated methodology described by ref. 73 ) and a downstream primer (YFP_R) that introduced a rigid linker 74 to separate the functional domains of the two amplified proteins (YFP and PilT). These two amplified fragments were then combined by secondary PCR, ligated into the linearized vector pGEM-T (Promega) and transformed via electroporation into E. coli S17-1, a broad-host-range donor strain. We then used a previously established protocol for using a mini-Tn7 system to insert our pilT-yfp construct into the chromosome of our ΔpilT strain at its single att Tn7 site (ref. 75 ; ΔpilT att Tn7:: pilT-yfp ). Doing so restored the motility of our ΔpilT strain to wild-type levels, thus confirming that our PilT–YFP fusion protein is functional when expressed from the BG35 promoter at the chromosomal att Tn7 site (Extended Data Fig. 7 ). The final construct was confirmed by sequencing.

All strains were grown from frozen stocks overnight in Luria–Bertani (Lennox) broth (Fisher, 37 °C, 250 r.p.m.) and sub-cultured (1:30 dilution) in tryptone broth (TB, 10 g l −1 , Bacto tryptone) for 2.5 h to obtain cells in exponential phase. Cells were then diluted to an optical density at 600 nm of either 0.15 (experiment shown in Fig. 2 and Extended Data Figs. 2 – 5 ) or 0.5 (all other experiments) in TB media before being used to inoculate microfluidic experiments.

In the Taylor–Aris dispersion experiments (Fig. 2 and Extended Data Figs. 2 – 6 ), we used a Nikon Ti2-E inverted microscope equipped with a ‘Perfect Focus’ system and a Hamamatsu Orca-Fusion camera. For the experiment shown in Extended Data Figs. 1 , 7 and 10 , we used a Nikon Ti-E inverted microscope equipped with a ‘Perfect Focus’ system, a Hamamatsu Flash 4.0 v2 camera and a CoolLED pE-4000 illuminator. For the experiments that quantified the distribution of PilT–YFP (Figs. 3a–c , 4 and 5 and Extended Data Fig. 8 ), we used a Zeiss Axio Observer inverted microscope equipped with a ‘Definite Focus’ system, a Zeiss AxioCam MRm camera, and a Zeiss HXP 120 illuminator. We used ×20 Plan Apochromat air objectives throughout, except for our studies of the subcellular localization of our PilT–YFP fusion protein, which used a ×63 Plan Apochromat oil-immersion objective (on the Zeiss system). Time lapse images were collected using the Zen Blue 2012 (Zeiss) and NIS-Elements AR v4.51.01 (Nikon) software on the Zeiss and Nikon systems, respectively.

Microfluidic experiments

Our custom-designed devices were cast with polydimethylsiloxane (PDMS) (Sylgard 184, Dow Corning) using moulds fabricated from SU-8 on silicon wafers (FlowJEM). Holes for tubing were punched through the PDMS using a Harris Unicore 1.5 mm biopsy tool (Agar Scientific). The PDMS was then bonded to glass coverslips (50 mm by 75 mm, number 1.5 thickness, Agar Scientific) using a corona treater (BD-20AC, Electro-Technic Products), as previously described 76 .

We plumbed the inlets and outlets of our microfluidic devices using Tygon microbore tubing (1.5 mm outside diameter) and then placed the entire set-up in a vacuum chamber for 1 h to reduce the potential for air bubbles. The devices were then mounted onto the microscope, and the outlet tubing was connected to a 10 ml plastic syringe (Luer-Lok, Becton Dickinson) using a 23-gauge needle (PrecisionGlide, Becton Dickinson). The syringe was filled with nutrient media (TB) and mounted onto a syringe pump (PhD Ultra, Harvard Apparatus). To remove air from the system, we first injected TB through the device at a flow rate of 100 µl min −1 . Exponential-phase cells (as described above) were then drawn into the device via suction at a flow rate of 50 µl min −1 through the inlet tubing. Once cells reached the test section of the channel, all inlets and outlets were clamped using haemostats for 10 min, which allowed cells to attach in the absence of any flow. After this attachment period, the TB from the syringe was injected through the device at 100 µl min −1 for 10 min to remove any remaining planktonic cells. Last, the ends of the inlet tubing were placed into new reservoirs, and fluid was pulled through the device via suction for the remainder of the experiments.

The experiments shown in Extended Data Figs. 1 , 7 and 10 were performed using the commercial BioFlux 200 microfluidic system (Fluxion Biosciences), using protocols that have been previously described 33 . We used our previously described model to quantify the chemical gradients within this device 77 .

Taylor–Aris dispersion microfluidic experiments

For the experiments shown in Fig. 2 and Extended Data Figs. 2 – 5 , we used a custom microfluidic device with a single inlet and outlet at either end of a rectangular microfluidic channel (30 mm in length with a cross section 1 mm wide and 75 µm deep). The inlet was connected to a 2 m length of Tygon tubing whose other end was placed in a reservoir containing TB mixed with succinate, and the entire system was filled with this fluid. Subsequently, we moved the end of the tube to another reservoir, containing a different concentration of succinate. When this new fluid was drawn into the tube via suction, Taylor–Aris dispersion mixed the interface between the media containing the two different concentrations of succinate longitudinally along the length of the 2 m tube before it flowed over the top of the cells. Alternatively, for control experiments, the end of the inlet tube was inserted into reservoirs that both contained TB with 1 mM succinate. Thus, cells in these control experiments did not experience any chemical gradients.

As discussed in the main text, our Taylor–Aris dispersion experiments were designed to expose cells to approximately the same mean concentration ( C ) and temporal concentration gradient (d C /d t ) that cells experienced in the dual-flow experiments where pili-based chemotaxis towards succinate was readily observed (Extended Data Fig. 1 and ref. 33 ). In these experiments, the static spatial gradient of succinate had a magnitude of approximately d C /d x  = 0.02 mM μm −1 . Individual twitching cells moved along this gradient with an average speed of V C  = 0.2 μm min −1 (Extended Data Fig. 1c ) and thus experienced a temporal gradient of succinate on the order of d C /d t  =  V C d C /d x  = (0.2 μm min −1 ) × (0.02 mM μm −1 ) = 0.004 mM min −1 . Cells in this region of the device experienced an absolute concentration of succinate of C  ≈ 1 mM.

Compared to flagella-based swimming, the motility of surface-attached P. aeruginosa cells is relatively slow, and reversals are relatively rare—on average, a cell reverses direction only once every several hours 33 . To ensure that our results were statistically robust, we aimed to collect as many cell trajectories (and thus reversals) as possible over the course of a Taylor-Aris dispersion experiment. To achieve this, we first used an automated microscope stage to simultaneously image 6 different fields of view within each microfluidic channel every minute (a total of 12 different scenes as we imaged in two channels simultaneously). Second, we aimed to expose cells to a temporal change in succinate concentration that lasted a period of approximately 1 h, so that we could detect a sufficient number of reversals over this period (labelled t 2 in Fig. 2b–d and Extended Data Figs. 4 and 5 ).

The length scale of the succinate gradient that forms along the length of the inlet tube is set by competition between molecular diffusion in the radial direction and differential advection in the longitudinal direction of the tube, such that the length scale of the gradient in the tube increases with the flow rate. To obtain succinate gradients with the correct magnitude, we used previously described theory 44 to design our experimental procedure. We first inserted the end of the inlet tube into the reservoir containing succinate at the higher concentration, C MAX , and then filled the entire microfluidic system with this media via suction. Then we switched the inlet tube to the reservoir containing the lower succinate concentration, C MIN , and pulled this second media into the inlet tube at a rate of 20 μl min −1 for 10 min. This formed a succinate gradient within the tube leading to the microfluidic device. We then lowered the flow rate on our syringe pump to 2 μl min −1 for the remainder of the experiment. The recently attached cells were allowed to adapt to the surface for approximately 2 h (under a continuous flow rate of 2 μl min −1 ) before the baseline measurements were recorded (white region labelled t 1 in Fig. 2b,c ).

We observed that the succinate gradient took approximately τ  = 60 min to pass through the microfluidic channel, as visualized by using dye (Chicago Sky Blue 6B, 0.5 mg ml −1 ) in each run of the experiment (for example, Fig. 2b ). This dye does not affect pili-based chemotaxis in P. aeruginosa 33 and is predicted to have approximately the same distribution as the succinate given that they both have a similar molecular weight. We chose C MAX  = 1.16 mM and C MIN  = 0.84 mM, which yielded a d C /d t  ≈ ( C MAX  −  C MIN )/ τ  = (1.16 mM − 0.84 mM)/60 min = 0.005 mM min −1 and ensured that cells experienced an average concentration of 1 mM succinate over the course of the experiment, which also matched the uniform succinate concentration used in control experiments. Our Taylor–Aris dispersion experiments thus closely matched the mean temporal gradient and mean concentration of succinate observed in the previously described dual-flow experiments (d C /d t  ≈ 0.004 mM min −1 and C  ≈ 1 mM, respectively).

The cells in our Taylor–Aris dispersion experiment primarily experience temporal variations in concentration that result from the spatial gradient of succinate flowing past them. We note that the speed of cells in our experiment V C  = 0.2 μm min −1 (Extended Data Fig. 1c ) is orders of magnitude smaller than the speed at which the succinate gradient passes through the device (approximately 27,000 μm min −1 ), so a cell’s movement relative to the gradient has no appreciable impact on the temporal variation in succinate concentration they experience. Moreover, the length scale of the succinate gradient when it passes through the test section of the microfluidic device is approximately L  = (27,000 μm min −1 ) × (60 min) = 1.6 m. Thus, the spatial gradient of succinate that cells experience across the length of their bodies in the Taylor–Aris dispersion experiments can be estimated as d C /d x  ≈ ( C MAX  −  C MIN )/ L  = (1.16 mM − 0.84 mM)/1.6 m = 2.0 × 10 −7  mM µm −1 , which is several orders of magnitude smaller than the spatial gradients that cells experienced in the dual-flow experiments (d C /d x  ≈ 0.02 mM μm −1 ).

In summary, the cells in the Taylor–Aris dispersion experiments experience approximately the same mean temporal stimuli as they do in the previous dual-flow experiments, while experiencing spatial gradients that are only vanishingly small in comparison.

To follow cell motility in these experiments, images were captured in brightfield at a rate of 1 frame per min. Using Fiji (v2.0.0) 78 , we stabilized the time series of brightfield images using the Image Stabiliser plugin to remove drift in the x , y plane. Next, the background was made more homogenous using the Normalise Local Contrast plugin, and the intensity of the background was reduced using the Subtract Background feature. Finally, a bleach correction plugin was used to correct for long-term changes in the relative pixel intensity of the cells in brightfield compared to the background, which varies as the concentration of dye changes over time 79 . Cells were then tracked using the Trackmate (v2.3.0) plugin for Fiji (v1.5.4) 80 . Finally, to analyse cell motility and to detect when cells reverse direction, we used an image analysis pipeline in Matlab (2019b) that we developed previously to study twitching motility in P. aeruginosa 33 .

Cell responses to sharp temporal changes in concentration

Twitching motility is characteristically jerky, and cells frequently undergo rapid displacements caused by the release of single pili, causing them to briefly move ~20 times faster than their average speed 38 , 50 . While these rapid displacements constitute a relatively small fraction of a cell’s total movement time, their contribution to a cell’s total displacement is approximately equal to their slower and steadier form of movement 50 . As noted in the main text, the temporal gradient that a cell experiences is linearly proportional to its movement speed (as d C /d t  =  V C d C /d x ), and so a cell is predicted to experience temporal gradients that are ~20 times larger during these rapid displacement events. We thus tested the possibility that twitching cells in the presence of chemical gradients might employ a temporal sensing modality that is tuned to respond to these relatively short but steep temporal chemoattractant gradients.

For these experiments, we used a dual-inlet BioFlux 200 microfluidic system (Fluxion Biosciences) in which one inlet was connected to TB mixed with a larger concentration of succinate ( C MAX  = 1.16 mM), while the other inlet was connected to TB mixed with a smaller concentration of succinate ( C MIN  = 0.84 mM). Instead of passing fluid through both inlets simultaneously so they formed a spatial gradient within the test section 33 , we instead passed fluid through only one inlet at a time, which exposes all cells in the test section to the same succinate concentration. We used computer-controlled software to alternate the flow between the two inlets, such that cells sequentially experienced a rapid increase in succinate concentration followed by a rapid decrease in succinate concentration over time. Like the Taylor–Aris dispersion experiments described in the previous section, we chose these C MAX and C MIN values so that the mean succinate concentration that cells experienced was 1 mM, which was the concentration where chemotaxis was observed to peak in the dual-flow experiment where cells where exposed to a spatial gradient of succinate.

We added Chicago Sky Blue 6B dye (0.5 mg ml −1 ) to the media containing the higher concentration of succinate ( C MAX ), whereas the media containing the lower concentration of succinate ( C MIN ) did not contain dye. By quantifying the change in dye intensity at the downstream end of the test section of the device, we observed that cells experienced a smooth change in concentration between the two different media over a timescale of τ  ≈ 1.5 min (Extended Data Fig. 6 ). Because the time period of the temporal gradient ( τ ) in these experiments is relatively short and therefore affords less time to observe reversals, we alternated the flow between the two inlets every 15 min so that we could expose cells to at least six increases and decreases in succinate concentration over the course of one experiment (Extended Data Fig. 6a ). We observe that the transition between the two succinate concentrations occurs smoothly and consistently in the test section of the device. We note that the overall duration of our microfluidic experiments is limited because in situ cell division eventually crowds the surface, which makes tracking individual cells difficult.

We can estimate the temporal gradient in these experiments as d C /d t  ≈ ( C MAX  −  C MIN )/ τ  = (1.16 mM − 0.84 mM)/1.5 min = 0.2 mM min −1 ; Extended Data Fig. 6b,c ), which is one to two orders of magnitude larger than the temporal gradients that cells were exposed to in the Taylor–Aris dispersion experiments described in the previous section and is approximately the same strength as the temporal stimuli that we predict a cell in our dual-flow experiments will experience momentarily during pili release events 38 , 50 . We can estimate the spatial gradients that form over the length of the test section in these experiments as d C /d x  ≈ ( C MAX  −  C MIN )/( Uτ ) = (1.16 mM − 0.84 mM)/(2,500 µm min −1  × 1.5 min) = 8.5 × 10 −5  mM µm −1 , (where U is the mean flow speed), which is approximately 200-fold smaller than the spatial gradients that cells experienced in the dual-flow experiments (d C /d x  ≈ 0.02 mM μm −1 ).

To follow cell motility, two fields of view were imaged in brightfield at a higher frame rate of 7.5 frames per min. Using Fiji 78 , images were processed and tracked using the Trackmate plugin 80 as described above. To analyse cell motility and to detect when cells reverse direction, we once again used our previously developed image analysis pipeline in Matlab 33 .

To test whether cells can sense and respond to this larger temporal stimulus, we compared cell reversal rates before, during and after they experienced a temporal gradient in succinate concentration across six increases and six decreases in succinate concentration (Extended Data Fig. 6b,d ). Our statistical analyses found that neither an increase nor a decrease in succinate concentration elicited cells to change their reversal rate (Extended Data Fig. 6c,e ). These experiments thus show that surface-attached P. aeruginosa cells do not respond to the larger temporal gradients that they would experience during pili release events.

Quantifying PilT–YFP sub-cellular localization

To measure how the localization of our PilT–YFP fusion protein varies from a cell’s leading pole to its lagging pole, we developed an image analysis pipeline that automatically tracks cell position, length and orientation in brightfield and uses this information to quantify the distribution of YFP using the corresponding epi-fluorescence images. Brightfield images were captured at a frame rate of 7.5 frames per min, while epi-fluorescence images to visualize YFP were simultaneously acquired at a lower frame rate of 0.5 frame per min. The higher frame rate for brightfield allowed us to track cell motility with sufficient accuracy, whereas the lower frame rate for the YFP imaging allowed us to avoid bleaching and phototoxicity.

All preliminary image analysis was conducted in Fiji 78 . Brightfield images were processed as outlined above. Epi-fluorescence images were processed in the same way as brightfield images, except we additionally used a Difference of Gaussian filter to enhance the contrast of the localized accumulations of PilT–YFP.

The cells in these processed images were then tracked using software called the Feature-Assisted Segmenter/Tracker (FAST v2.1 81 ; https://mackdurham.group.shef.ac.uk/FAST_DokuWiki/dokuwiki/doku.php?id=start ) which allowed us to track cell position and orientation with greater precision compared to the tracking plugins available in Fiji. To map how the distribution of PilT–YFP varies along the cell length and how that distribution changes as cells move, we used FAST to calculate the cell centroid, length and orientation of each cell in the brightfield images. We then used this information to calculate the position of the ‘centreline’ of each cell (that is, a line that passes through the middle of a cell along its major axis) on the corresponding YFP epi-fluorescence image. However, PilT–YFP localizations do not always occur exactly along the predicted centreline—but rather they were sometimes found slightly to one side of the centreline. Thus, to accurately quantify the distribution of the fusion protein, we needed to develop a method that could detect PilT–YFP localizations even when they were offset slightly from the cell’s centreline, in addition to being robust to small amounts of cell movement that occurred in the time interval between when the brightfield and YFP images were captured. To account for these factors, we used the Matlab function ‘improfile’ to calculate the YFP fluorescence intensity along a series of 10 parallel lines with the same orientation and length as a cell but separated by a small distance (0.09 µm) from one another so that collectively they spanned a width approximately equal to the width of the cells (~0.9 µm). We then calculated the maximum YFP intensity at fixed intervals along the length of these ten lines to obtain the maximum fluorescence intensity at each position along the cell’s length. This process was used to record the distribution of PilT–YFP along the length of each cell at every time point across the three bio-replicate experiments ( n  = 52,179 trajectory points).

A small number of cell fragments and other detritus were occasionally observed in the brightfield images we used to segment cells; however, these generally were not visible on the corresponding YFP images. To prevent these from inadvertently being included in our analyses, we measured the mean YFP intensity of all objects using the segmentations obtained from the brightfield image and removed trajectories without appreciable YFP signal from subsequent analyses. We also omitted any cells with an aspect ratio smaller than 1.4, which ensured that our analyses only included cells that were attached to the surface by both cell poles.

We next quantified the distribution of PilT–YFP fusion protein within the poles of the cells. Because the maximum YFP intensity often does not occur at the very tip of the pole, we measured the maximum YFP intensity in the vicinity of the poles. The cell length was measured using YFP images, and the maximum YFP intensity was calculated in the two regions at either end of the cell, each corresponding to one tenth of the overall cell length. To classify the distribution of PilT–YFP within a cell as nonpolar, unipolar or bipolar (Fig. 3 ), we normalized the maximum YFP intensity within each pole by the mean YFP intensity in the central one fourth of the cell. If the normalized YFP intensity in a given pole (denoted as I 1 and I 2 for pole 1 and pole 2) exceeded a threshold I MIN , the protein was considered to have aggregated within that pole. More specifically, if both I 1  >  I MIN and I 2  >  I MIN , the cell was considered bipolar, whereas if either I 1  >  I MIN or I 2  >  I MIN , the cell was considered unipolar. Last, if I 1  <  I MIN and I 2  <  I MIN , the cell was considered nonpolar. To determine the value of I MIN for a given bio-replicate, we calculated the normalized YFP intensity values ( I 1 and I 2 ) for all cells in a YFP image, which had been processed as described above. This allowed us to choose an I MIN value by visual inspection that correctly distinguished cell poles with PilT–YFP localization from those that lacked PilT–YFP localization.

To increase the accuracy of the automated assignment of cells as bipolar, unipolar or nonpolar, we also implemented the following two rules:

When P. aeruginosa nears cell division, the pili machinery (and thus the PilT–YFP protein fusion) begins to localize additionally to the nascent cell poles, which are positioned at mid-cell 82 . In such instances, the maximum fluorescence intensity can occur in the mid-cell region rather than at the poles. As we are interested in the processes underlying cell motility (rather than cell division), we excluded cells from our analyses whose average fluorescence in the middle one fourth of their bodies was larger than that found in either of the poles.

Some cells that were initially assigned as bipolar (that is, because the YFP intensity at its two poles, I 1 and I 2 , both exceeded I MIN ) were re-assigned as unipolar if the YFP signal in one of their poles was much stronger than in their opposite pole. To detect such instances, we plotted the ratio of I 1 and I 2 , dividing the larger YFP intensity by the smaller one, for each cell in our processed YFP images. We then used the threshold that best distinguished bipolar cells from unipolar ones by direct visual inspection. This allowed us to ensure that we assigned cells with strongly asymmetrical patterns of PilT–YFP localization as ‘unipolar’, rather than ‘bipolar’.

To compare the distribution of PilT–YFP in stationary and moving cells, we classified trajectories by their speed (Fig. 3 ). Due to pixel noise and the effect of fluid flow, the measured trajectories of non-motile cells showed a finite velocity. To account for these effects, we classified cells moving slower than 0.038 μm min −1 as ‘stationary’, whereas cells moving faster than this threshold were classified as ‘motile’. To prevent cells simply jostling back and forth from being considered motile, we additionally removed trajectories from the motile category whose net to gross displacement ratio (NGDR) was less than 0.04. In addition, we excluded cells that were actively rotating from the motile category by identifying cells whose bodies had an angular velocity larger than 0.073 radians min −1 for a contiguous period of longer than 2 min. These angular velocities were obtained from measurements of cell orientation that had been smoothed with a first-order Savitzky–Golay filter (using a 20 min window) to reduce noise. All the parameters used in these analyses were extensively ground-truthed to ensure that they had the desired effect.

Generating alternating spatial chemoattractant gradients

To expose cells to a spatial chemoattractant gradient that alternates in direction by 180°, we used a custom microfluidic device described in detail previously 33 . Briefly, the device is composed of a Y-shaped channel with four inlets (two inlets in each branch of the Y) and a single outlet that was connected to a syringe pump.

In these experiments, a steady spatial gradient of succinate forms along the centreline of the device, where the fluids from two inlets located in opposite arms of the Y-shaped channel meet one another. The fluid supplied through one arm contained nutrient media (TB) supplemented with 2 mM of succinate and Chicago Sky Blue 6B dye (0.5 mg ml −1 ), whereas fluid from the other arm contained only undyed nutrient media. Molecular diffusion generated a stable gradient of succinate across the width of the channel, which could be readily quantified by imaging the dye as they have a similar diffusion coefficient.

The syringe pump pulled media through the device via suction (5 μl min −1 ) from reservoirs connected to the four inlets of the device. A haemostat was used to clamp the tubing connected to two of the inlets at any given time. To change the direction of the gradient, the haemostat is removed from one pair of tubes and transferred to the other pair, which contain the same two fluids but in the opposite orientation (see ref. 33 for details). We changed the direction of the gradient approximately every 45–60 min, and we monitored cells for repolarization events for as long as possible after the gradient swap, before we needed to pause the imaging to set up the next gradient swap.

Brightfield images were captured at a frame rate of 7.5 frames per min so that changes in the gradient and cell movement could be tracked at a high temporal resolution. Epifluorescence images of the cells were captured at a slower frame rate of 0.4 frame per min to avoid bleaching of the PilT–YFP fusion protein and to prevent phototoxicity. The details of how cells were tracked and how the distribution of the fusion protein inside them was quantified is outlined below.

Analysing PilT–YFP localization in stationary cells

To directly test whether surface-attached P. aeruginosa cells are capable of spatial sensing, we exposed our ΔpilT :: pilT-yfp strain to a spatial gradient of succinate that alternated direction using the microfluidic device outlined in the previous section. To exclude the possibility that cells could use temporal sensing to determine the orientation of the new succinate gradient, we only considered repolarization events that occurred in stationary cells (see main text). Because the PilT–YFP protein fusion tends to localize to a cell’s leading pole (Fig. 3c ), each repolarization event can be categorized according to whether the new leading pole of a stationary cell is oriented towards (‘correct’) or away from (‘incorrect’) increasing succinate concentrations following the change in gradient orientation (Fig. 4 ). In addition, we also classified repolarization events according to whether PilT–YFP was initially localized in both poles (bipolar), in only one pole (unipolar) or in neither pole (nonpolar) before the repolarization event occurred (Fig. 3g–i ).

While our other analyses used automated cell tracking to quantify cell behaviour, we decided to detect and classify these repolarization events manually for two main reasons. First, a relatively small number of repolarization events are observed in these experiments, so we wanted to follow the behaviour of every single cell and rigorously ground-truth all putative repolarization events to confirm that they were not erroneous. Secondly, many stationary cells reside in densely packed groups, which help to stifle movement. However, densely packed cells are challenging to track using automated methods without occasional errors, and it is difficult to measure an individual cell’s PilT–YFP distribution without inadvertently having it contaminated by the YFP signal produced by neighbouring cells. (Note that in other experiments that were analysed using automated cell tracking, we developed filters to specifically exclude cells that were clustered together.)

We analysed the behaviour of every cell that was visible in the 16 different fields of view collected over the course of three separate microfluidic experiments (Supplementary Videos 1 – 16 ) and classified them with a detailed set of rules outlined in the following four sub-sections below. Out of >1,000 cells that were investigated, we identified 171 stationary cells that performed a repolarization event—as defined by these rules—following the gradient swap. To prevent potential errors, a preliminary list of repolarization events was independently assessed by two co-authors (J.H.R.W. and W.M.D.), and any discrepancies were reconciled before our final analyses. All 171 repolarization events are labelled in Supplementary Videos 1 – 16 , along with the details of how each was classified (Supplementary Table 1 ).

Below we describe in detail the rules that were used to define and classify each putative repolarization event.

Identifying when a repolarization event occurs

We search for potential repolarization events in cells that are stationary after the succinate gradient changes direction. In many cases, stationary cells first localize PilT–YFP exclusively to their new leading pole before moving off; however, sometimes cell movement occurs first. A repolarization event therefore occurs as soon as a stationary cell either (A) develops a unipolar pattern of PilT–YFP localization that is different from that of its initial localization of PilT–YFP or (B) moves off in a direction different from that of its initial localization of PilT–YFP. In the first case, (A), a cell must re-localize PilT–YFP to a single pole in at least two of four consecutive frames (10 min), whereas in the second case, (B), a cell must move off in a consistent direction for at least two frames at a speed corresponding to at least one cell width per frame.

Following a repolarization event, we define a cell’s ‘new leading pole’ as the one that either contains the new unipolar PilT–YFP localization or leads its initial movement, whichever has occurred first. The orientation of a cell’s ‘new leading pole’ after the repolarization event is then used to determine whether it can be classified as a ‘correct’ or ‘incorrect’ repolarization event by comparing its orientation relative to that of the new succinate gradient (Fig. 4a–c and Supplementary Table 1 ).

Importantly, for a repolarization event to have occurred, a cell must not have previously had a unipolar PilT–YFP localization in the ‘new leading pole’ in either two or more of the four frames (10 min) that precede the appearance of the new succinate gradient, or within the frame that immediately precedes the appearance of the new succinate gradient. This requirement thus ensures that cells have actively changed their distribution of PilT–YFP following the change in gradient direction and also prevents short-lived, random fluctuations in the distribution of PilT–YFP from being erroneously classified as a repolarization event.

Defining which cells are considered ‘stationary’

These experiments aim to analyse the behaviour of stationary cells because motile cells could potentially use temporal sensing to determine the orientation of the new succinate gradient. However, cells can sometimes show small amounts of movement that are unrelated to their motility. For example, the flow in our experiments tends to push cells downstream while cells at the periphery of densely packed cell clusters can get pushed radially outwards by their neighbours as the cluster grows. As such movements are not under the active control of a cell, they could not encode information about the direction of a gradient via temporal changes in succinate concentration in the same way that active motility would. In addition, in our experiments, cells that are pushed a small distance by flow tend to move in the direction orthogonal to the gradient and thus do not experience appreciable changes in succinate concentration over time. We therefore monitor a cell’s movement in the direction along the gradient to ensure it is sufficiently small in the period preceding a repolarization event.

To determine whether a cell can be considered stationary, we monitor its movement from the frame after the last frame in which the initial succinate gradient was present until the frame in which the cell undergoes a repolarization event. However, as some repolarization events occur shortly after the gradient has changed direction, we also monitor cell movement for at least three frames (7.5 min) before any putative repolarization event. A cell is then considered ‘stationary’ within these time periods provided that its centroid neither (a) moves more than half a cell width in the same direction for two consecutive frames nor (b) moves more than one cell width at any point. All distances are measured along the direction of the chemical gradient, and a cell width is approximately 0.9 µm.

Note that many cells are stationary for a finite period, and so a cell that is currently stationary will likely have moved at some point in the past. Our analyses include cells that move while the initial succinate gradient is still present but subsequently stop moving before the gradient starts to change direction. This is because such previous movement could not inform a cell that the orientation of the succinate gradient will change later in the experiment.

Assigning a cell’s polarity before repolarization events

We categorize repolarization events according to the PilT–YFP localization that they previously showed (Figs. 3g–i and 4e ). For a repolarization event to be assigned as either nonpolar, unipolar or bipolar, the cell must have had that polarity mode more frequently than any other in the four frames (10 min) preceding the appearance of the final gradient orientation. If two different polarity modes are each present for two frames apiece, then we assign the polarity mode that occurs in the frame immediately preceding the appearance of the final gradient orientation. We note that the ‘initial polarity’ of two cells could not be resolved in these experiments because one of their poles was initially in very close proximity to that of their neighbours, which prevented us from distinguishing the YFP signal that belonged to each cell. Thus, the initial polarity of these two cells was classified as ‘not assignable’ in Supplementary Videos 1 – 16 and Supplementary Table 1 .

We also observed a small number ( n  = 13) of repolarization events in newly divided cells. If a cell that is stationary (as defined above) divides shortly after the change in gradient orientation, one or both of the resulting daughter cells could in theory undergo a repolarization event (as defined above). In these cases, the distribution of PilT–YFP is assigned as nonpolar, unipolar or bipolar (Figs. 3g–i and 4e ) according to the most frequent localization pattern in the frames between the cell division event and the subsequent repolarization event. We did not consider PilT–YFP localizations at the midpoint of the mother cell before cell division in our analyses, because they are not necessarily related to motility and can be asymmetrically divided between the two daughter cells during septation 82 .

Assigning temporal changes in succinate concentration

As above, we used Chicago Sky Blue 6B dye to visualize the alternating succinate gradient. When the gradient changes orientation by 180°, cells initially situated in regions of low succinate concentration ( C  <  C MAX /2, as determined by the dye intensity) experience a temporal increase in succinate concentration, whereas those initially in regions of high succinate concentration ( C  >  C MAX /2) experience a temporal decrease in succinate concentration. By following changes in the dye intensity, we were able to group repolarization events according to whether they occurred in cells that had experienced an overall increase or decrease in succinate concentration (Extended Data Fig. 8 ). However, it was very difficult to distinguish the small temporal changes in succinate concentration (and thus dye intensity) experienced by cells situated close to the centreline of the spatial gradient. These cells ( n  = 10) were therefore excluded from the analyses that compared the response of cells experiencing a step-up in succinate concentration to a step-down in succinate concentration. The ‘temporal change in [succinate]’ of these cells is marked as ‘not assignable’ in Supplementary Videos 1 – 16 and Supplementary Table 1 .

Methods used to illustrate repolarization events

We used automated cell tracking software (Trackmate plugin, Fiji 78 , 80 ) to follow cell movement across four exemplar repolarization events to quantify changes in cell speed and to map changes in succinate concentration at the location of each of the four cells (Fig. 5a–d ). To ensure that we could obtain trajectories that spanned the entire length of experiment, the cells of interest were cropped out frame by frame using the ‘Brush Tool’ included with Fiji (v2.0.0). This left us with only a single cell visible in the entire time series of images, ensuring the automated tracking was not influenced by the presence of neighbouring cells. We used the resulting curated trajectories to calculate a cell’s position relative to the chemoattractant gradient (grey lines in Fig. 5a–d ). The concentration of succinate that a cell experienced over time (black lines in Fig. 5a–d ) was quantified using the Chicago Sky Blue 6B dye, which was mixed with the 2 mM succinate solution. The distribution of dye was imaged using brightfield microscopy, and separate experiments showed a linear dependence between the pixel intensity and dye concentration, allowing us to easily estimate the succinate concentration at the position of each cell within the device.

Statistical analyses

To test whether cells use temporal chemoattractant gradients to guide pili-based motility, we developed statistical methods to determine whether cells alter their reversal rate in response to temporal gradients of succinate in comparison to control conditions where the concentrations of succinate were constant. Our Taylor–Aris dispersion experiments (Fig. 2 and Extended Data Figs. 3 – 5 ) are ~3 h long, and the total number of cells changes over this timescale due to cell detachment from and attachment to the surface, as well as continued cell division (Extended Data Fig. 2 and ref. 46 ). Furthermore, even in the absence of any chemical gradients, reversal rates change over time, likely driven by cells undergoing physiological adaptation following surface attachment (refs. 47 , 48 ) (Extended Data Fig. 3 ). To take these temporal trends into account, we divided our datasets into three time bins corresponding to before, during and after the cells experienced a temporal gradient of succinate (see t 1 , t 2 and t 3 in Fig. 2 and Extended Data Figs. 4 and 5 ).

Our Taylor–Aris dispersion experiments imaged six different fields of view simultaneously at a frame rate of 1 frame per min, yielding several thousand trajectories at each time point (Extended Data Fig. 2a–c ). However, reversals are relatively rare—on average a cell reverses direction only once every several hours. Our datasets therefore consist of a very large number of time points at each of which a cell can either carry on moving in a relatively straight line or, with a low probability, reverse direction. We therefore assume that reversals are Poisson distributed, allowing us to calculate the confidence intervals of our reversal rate estimates. Using this assumption, we also used the exact Poisson test (using the ‘poisson.test’ function (v3.6.2) in R (v4.2.3)) to test for differences in reversal rates between control and experimental conditions (Fig. 2 and Extended Data Figs. 4 and 5 ).

A similar approach was used to generate confidence intervals for our estimates of reversal rates for cells moving either up or down spatial chemoattractant gradients (Extended Data Figs. 7 and 10 ). However, in these analyses, we calculated the mean reversal rate using data from the entire experiment (rather than subdividing it into different bins in time), because in these experiments, the gradient was present for the entire duration.

Strain availability

The bacterial strains used in this study are available from the corresponding authors upon request.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

Source data are provided with this paper. Image data (~650 GB) is available from the corresponding authors upon request. All other data that support the findings of this study can be accessed at https://doi.org/10.15131/shef.data.25800409 .

Code availability

The code used to generate the findings of this study can be accessed at https://doi.org/10.15131/shef.data.25800409 .

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Acknowledgements

We thank J. N. Engel (University of California, San Francisco, UCSF) and Y. F. Inclan (UCSF) for strains and plasmids; L. Vanderpant (Digital Pixel) for help designing microscope incubation chambers; D. Gonzalez for help with cloning; J. P. Armitage for advice; O. J. Meacock for help with automated cell tracking and S. C. Booth, O. J. Meacock and M. D. Koch for providing feedback on a previous version of this manuscript. This work was funded by a BBSRC DTP studentship (BB/J014427/1) awarded to J.H.R.W.; the Human Frontier Science Program (LT001181/2011L and RGY0080/2021), EPSRC Pump Priming Award (EP/M027430/1) and BBSRC New Investigator Grant (BB/R018383/1) to W.M.D.; and by the European Research Council grant 787932 and Wellcome Trust Investigator award 209397/Z/17/Z to K.R.F.

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J.H.R.W., K.R.F. and W.M.D. designed the research; J.H.R.W. performed research; J.H.R.W. and W.M.D. analysed data; J.H.R.W., K.R.F. and W.M.D. wrote the paper.

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Extended data

Extended data fig. 1 surface-attached p. aeruginosa cells climb spatial succinate gradients by actively changing the rate at which they reverse direction..

( a ) A dual-inlet BioFlux microfluidic device was used to expose cells to a spatial gradient of succinate ( C MIN = 0 mM, C MAX = 2 mM) with a characteristic length-scale of 100 µm, and an automated algorithm was used to detect when cells reversed their movement direction 33 . Reversals were classified as either ‘correct’ or ‘incorrect’. Correct reversals (green square) occur in cells that were initially moving away from the source of succinate, whilst incorrect reversals (black square) occur in cells that were initially moving towards the source of succinate. ( b ) The rate of correct reversals is significantly greater than that of incorrect reversals, which drives chemotaxis towards succinate. Crosses (‘X’) mark the mean of five separate TB-only control experiments, six separate succinate-only control experiments, or eleven separate succinate gradient experiments. In TB-only experiments, TB is passed through both inlets at the same time, whilst in succinate-only experiments, media containing succinate is passed through both inlets at the same time. Controls were processed in the same way as for succinate gradients, but since no gradient was actually present, the ‘correct’ and ‘incorrect’ rates shown are arbitrary. p -values were obtained from two-sided paired t -tests of the null hypothesis that the measured incorrect and correct reversal rates come from the same distribution. ( c ) Cell speed is significantly higher in the presence of succinate gradients compared to both control experiments and is significantly higher in succinate-only controls compared to TB-only controls (one-way ANOVA, p = 2.22 × 10 −11 ). The analysis in panels B and C only includes trajectory time-points that occurred before 5 h, after which the cell density becomes too high to reliably track cells in these experiments. The succinate gradient datasets additionally only include trajectory time-points above a minimum gradient strength of 0.0006 mM µm −1 to ensure that cells within regions of the device where there is a very small spatial gradient were not included in our analysis. Source data provided as a Source Data file.

Extended Data Fig. 2 The effect of succinate on cells in our Taylor-Aris dispersion experiments.

( a ) Thin lines show the number of cell trajectories that were imaged in each of the six simultaneously imaged fields of view that were used in our Taylor-Aris dispersion experiments, whilst the thick lines show the mean. We observed that the number of cells increased gradually over the course of our approximately 3 h long experiments, regardless of whether cells were exposed to a decrease in succinate concentration over time (red lines) or to a constant concentration of succinate C = 1 mM in control experiments (black lines). ( b ) Cell speed remained approximately constant both in controls (black lines) and in cells exposed to decreasing succinate concentration (red lines). ( c , d ) Similar trends were observed for cells exposed to an increase (green lines) in succinate concentration over time when compared to their respective controls (black lines). The data shown here is representative of both bio-replicates. Source data provided as a Source Data file.

Extended Data Fig. 3 Cell reversal rate decreases over time in our Taylor-Aris dispersion experiments and in their respective controls.

( a ) In experiments that exposed cells to a temporal increase in succinate concentration (blue bars), cell reversal rate decreased over the time course of the experiments. A similar decrease was observed in the corresponding controls (green bars) where cells were exposed to a constant succinate concentration C = 1 mM. Similar trends were observed in a second bio-replicate of this experiment ( b ) and in two bio-replicates where cells were exposed to a temporal decrease in succinate concentration ( c , d ). Source data provided as a Source Data file.

Extended Data Fig. 4 A temporal increase in succinate concentration does not induce a chemotactic response in surface-attached P. aeruginosa.

( a , b ) Using the approach outlined in Fig. 2 , cells were exposed to temporal increases in succinate concentration ( C MIN = 0.84 mM, C MAX = 1.16 mM; blue line). This generates mean temporal concentration gradients approximating the gradient experienced by cells moving towards increasing succinate concentrations in the dual-inlet chemotaxis experiments (Extended Data Fig. 1 ), but with 16,000-fold smaller spatial gradients. If cells can sense these temporal stimuli, the temporal increase in succinate concentration would be predicted to suppress reversals. ( c ) In the 1 h period before the succinate gradient entered the microfluidic device (interval t 1 ) cell reversal rates were statistically indistinguishable between experiment (white bar, blue outline) and control (white bar, green outline; one-sided exact Poisson test ( Methods ) yielded p = 0. 762). Similarly, the reversal rates in the presence of a temporal succinate gradient (interval t 2 ; light grey bar, blue outline) and in the 1 h period after the gradient had cleared the microfluidic device (interval t 3 ; dark grey bar, blue outline) were statistically indistinguishable from the reversal rates during the same time periods in the control ( p = 0.342 and p = 0.872). The number of reversals observed was n r = 2709 and 2980 across n t = 636,364 and 709,607 trajectory points in the control and experimental conditions respectively. Error bars show 95% confidence intervals about the estimated reversal rates assuming that reversals follow a Poisson distribution ( Methods ). ( d , e , f ) A second bio-replicate confirmed that when comparing between experiment and control, reversal rates were indistinguishable during time periods t 1 (white bars, p ≈ 1), t 2 (light gray bars, p = 0.077) and t 3 (dark gray bars, p = 0.468). p-values were obtained from a one-sided exact Poisson test ( Methods ); n r = 2101 and 2034 across n t = 536,892 and 504,264 trajectory points in the control and experimental conditions respectively. Error bars show 95% confidence intervals about the mean reversal rates, assuming that reversals follow a Poisson distribution ( Methods ). Source data provided as a Source Data file.

Extended Data Fig. 5 A temporal decrease in succinate concentration does not induce a chemotactic response in surface-attached P. aeruginosa.

( a , b ) Data shown come from a biological repeat of the experiment outlined in Fig. 2 , where cells were either exposed to a temporal decrease in succinate concentration over time (blue lines) or to a control with a constant succinate concentration C = 1 mM (dashed green lines). ( c ) Using automated reversal detection, we first confirmed that the reversal rate in the 1 h period before the succinate gradient entered the microfluidic device (time interval t 1 ; white bar, blue outline), was statistically indistinguishable from the reversal rate during the same time period in a simultaneous control experiment where a constant concentration of succinate was maintained throughout (white bar, green outline). Specifically, a one-sided exact Poisson test ( Methods ) did not reject the null hypothesis that these two reversal rate measurements come from the same Poisson distribution, p = 0.800. Similarly, the reversal rates in the presence of a temporal succinate gradient (time interval t 2 ; light grey bar, blue outline) and in the 1 h period after the gradient had cleared the microfluidic device (time interval t 3 ; dark grey bar, blue outline) were statistically indistinguishable from the reversal rates during the same time periods in the control ( p = 0.289 and p = 0.859). The total number of reversals observed in our six simultaneously imaged fields of view was n r = 772 and 1072 across a total of n t = 259,301 and 370,801 trajectory points in the control and experimental conditions respectively. Error bars show 95% confidence intervals about the mean reversal rates assuming that reversals follow a Poisson distribution ( Methods ). Source data provided as a Source Data file.

Extended Data Fig. 6 Steep, rapid temporal chemoattractant gradients do not cause surface-attached P. aeruginosa cells to change their reversal rate.

( a ) Twitching is characteristically jerky and cells could have evolved to detect the large but ephemeral temporal changes in chemoattractant concentration caused by these intermittent displacements. To test this hypothesis, we used a microfluidic setup that exposed surface-attached cells to rapid temporal changes in succinate concentration (see Methods ). We used dye to quantify temporal changes in succinate concentration (purple line) and the temporal succinate gradients (blue line) that cells experienced in these experiments. In this experiment, cells are repeatedly exposed to both increases and decreases in succinate concentration. ( b ) To analyse cells’ response to these different stimuli, we first split reversal data around each increase in succinate concentration into three time-bins t 1 , t 2 , and t 3 corresponding to the 4 min intervals before, during and after the temporal gradient. ( c ) Reversal rates were pooled across time windows t 1 and t 3 , corresponding to time periods without any succinate concentration gradients, and compared to the reversal rates during the temporal increases in succinate concentration, time-bin t 2 . The mean reversal rate measured during the temporal increase in succinate concentration (large black ‘-’ marker) was statistically indistinguishable from that when the succinate concentration was constant (a two-tailed, paired t -test of the null hypothesis of no difference in reversal rates yielded p = 0.991, 0.467 and 0.661 for three independent bio-replicates). Mean reversal rates were averaged across six subsequent increases in succinate concentration (see (A)) each imaged across two independent fields of view (the 12 circular markers are colour-coded to show pairs of data recorded in each of the 12 fields of view, see Methods ). ( d , e ) Similar results were obtained when comparing reversal rates between the presence ( t 2 ) and absence ( t 1 and t 3 ) of temporal decreases in succinate concentration (a two-tailed, paired t -test of the null hypothesis of no difference in reversal rates yielded p = 0.820, 0.164 and 0.437 for three independent bio-replicates). Error bars show mean reversal rates plus and minus standard error. Source data provided as a Source Data file.

Extended Data Fig. 7 The PilT-YFP fusion protein complements motility and chemotaxis phenotypes of a ΔpilT mutant.

( a ) Surface-attached P. aeruginosa cells within dual-inlet microfluidic devices (scale bars = 100 µm) were exposed to succinate gradients by flowing TB through one inlet and TB media supplemented with succinate ( C MAX = 2 mM) through the other. WT cells (white) undergo chemotaxis and accumulate at high succinate concentrations ( t =10 h). ( b ) P. aeruginosa cells lacking pilT ( ΔpilT ) have impaired twitching motility 55 and distribute equally across the device ( t = 10 h). ( c ) Our PilT-YFP translational fusion restores motility and chemotaxis when expressed in the ΔpilT strain ( ΔpilT :: pilT-YFP , t = 10 h). Images representative of two bio-replicates. ( d ) A probability density function of cell speed (first 300 min, when cells exhibit highest motility) confirms that the ΔpilT strain (blue line) has impaired twitching motility, which is restored by our PilT-YFP translational fusion (pink line). WT cell speeds (black line) are shown for reference. Cell speed was analysed in the y -dimension as cells are pushed slightly by fluid flow in the x -direction shown from left-to-right in panels A-C. ( e ) WT cells move up succinate gradients (white bars) faster than they move down them (grey bars), a trend also observed in our ΔpilT :: pilT-YFP strain (pink bars). However, this trend is lacking in a ΔpilT mutant, which has greatly reduced overall cell speeds. All bars show median cell speeds. ( f ) WT and our ΔpilT :: pilT-YFP strain deploy ‘correct’ reversals (white bars) more frequently than ‘incorrect’ reversals (grey bars, see Extended Data Fig. 1 ; a one-sided exact Poisson test ( Methods ) yields p = 0.0005 and p = 0.033 across a total of n = 89 and n = 156 reversals). We could not measure reversal rates for our ΔpilT strain because of its general lack of motility. Error bars show 95% confidence intervals centred on the mean reversal rates, assuming that reversals follow a Poisson distribution ( Methods ). Data shown is representative of two bio-replicates. Here, tryptone broth is ‘TB’ and succinate is ‘Succ’. Source data provided as a Source Data file.

Extended Data Fig. 8 Stationary cells are more likely to undergo repolarisation events when they have recently experienced a rapid decrease in chemoattractant concentration.

In the alternating gradient experiments (Figs. 4 and 5 ), cells are exposed to large temporal changes in succinate concentration. We observed a larger number of repolarisation events in cells that experienced a temporal decrease in succinate concentration (68% of the total) than those that experienced a temporal increase (32% of the total). Specifically, we rejected the null hypothesis that the proportion of repolarisation events that occurred in cells experiencing a temporal decrease in succinate concentration is equal to the proportion of those experiencing a temporal increase, ( p = 3.83 × 10 −6 , two-tailed hypothesis test, assuming a binomial distribution with n = 161 trials with probability of 0.5 in each trial). This suggests that the chemotactic response depends in part on the absolute chemoattractant concentration experienced by cells 57 , 58 , 59 . In both cases, ‘correct’ repolarisation events (green bars) were significantly more abundant than ‘incorrect’ ones (magenta bars). Specifically, we rejected the null hypothesis that incorrect and correct repolarisation events occurred with equal frequency when the concentration was increasing ( p = 1.98 × 10 −4 , one-tailed hypothesis test assuming a binomial process with n = 51 trials and probability of 0.5 in each trial) and for when the concentration was decreasing ( p = 8.01 × 10 −20 , one-tailed hypothesis test assuming a binomial distribution with n = 110 trials and probability of 0.5 in each trial). Lastly, we note that cells experiencing a decrease in succinate concentration were significantly more likely to perform correct reversals than those experiencing an increase in succinate concentration. This analysis used a two-tailed Fisher′s exact test to reject the null hypothesis that there was no association between the sign of the temporal succinate gradient and whether the repolarisation event was correct or incorrect ( p = 0.024). Source data provided as a Source Data file.

Extended Data Fig. 9 The chemotactic response of twitching cells is strongest at lower absolute concentrations.

Theoretical predictions suggest that the chemotactic response magnitude will increase with gradient strength (which we have previously observed for twitching cells 33 ,) and decrease with absolute concentration (see main text, and see 57 , 58 , 59 ). Using datasets from twitching cells in our standard dual-flow chemotaxis assays (see Extended Data Fig. 1 ), we binned up cell trajectories according to whether they were in the low (0 < C < 1 m, black markers; squares represent individual bio-replicates) or high (1 < C < 2 mM, red markers) concentration region of the gradient. The ‘x’ symbols show the mean of each across all bio-replicates. Note that because the gradient is symmetrical about C = 1 mM, the cells in these two regions experience equivalent gradient strengths. Despite being exposed to equivalent gradient strengths, the cells in the higher concentration region have a significantly higher rate of incorrect reversals and a significantly lower rate of correct reversals than cells in the lower concentration region, ( p -values show the results of two-sided paired t -tests). As in Extended Data Fig. 1b , this analysis only includes trajectory time-points that occurred before 5 h (that is, before the cell density becomes too high to reliably track cells in these experiments) and above a minimum gradient strength of 0.0006 mM µm −1 to ensure that cells within regions of the device where there is a very small spatial gradient were not included in our analysis. Source data provided as a Source Data file.

Extended Data Fig. 10 Cells lacking the response regulator, PilG, retain some degree of motility in microfluidic experiments 33 , but reverse at a lower rate than WT cells and can no longer bias reversals towards succinate.

( a , b , c , d ) Surface-attached P. aeruginosa cells (shown in white) within dual-inlet microfluidic devices (scale bars = 100 µm) were exposed to controls with a constant succinate concentration ( C = 2 mM, A,B) or to succinate gradients ( C MAX = 2 mM, C,D) for t = 10 h. In gradients, WT cells undergo chemotaxis and accumulate at high succinate concentrations, whilst this accumulation is absent in no gradient controls. P. aeruginosa cells lacking pilG (images with yellow outline) cannot respond to succinate gradients and do not accumulate in either the succinate gradient or in the control. ( e ) In gradient free controls, ΔpilG cells (yellow outline) reverse direction less frequently than WT cells (black outline), but both WT and ΔpilG cells were equally likely to reverse direction no matter if they were initially moving in a downwards (‘incorrect’ reversals, grey bars) or upwards (‘correct’ reversals, white bars) direction. A one-sided exact Poisson test ( Methods ) yielded p = 0.93 and p = 0.87 across a total of n = 129 and 38 reversals for WT and ΔpilG cells respectively. However, in the presence of a succinate gradient, WT reversal rates were significantly larger when cells were initially moving towards decreasing succinate concentrations (‘correct’ reversals, white bars; p = 0.00072, n = 218, see also Extended Data Fig. 1 ). In contrast, the reversal rates of ΔpilG cells that were initially moving up or down the succinate gradient were not significantly different ( p ≈ 1, n = 50). Error bars show 95% confidence intervals centred on the mean reversal rates assuming that reversals follow a Poisson distribution ( Methods ). Data is representative of three biological repeats. As in Extended Data Fig. 1b , this analysis includes trajectories from cells that were exposed to a succinate gradient >0.0006 mM µm −1 to exclude cells exposed to only very small spatial succinate gradients. Here tryptone broth is ‘TB’ and succinate is ‘Succ’. Source data provided as a Source Data file.

Supplementary information

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Supplementary table detailing and indexing all repolarization events.

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Wheeler, J.H.R., Foster, K.R. & Durham, W.M. Individual bacterial cells can use spatial sensing of chemical gradients to direct chemotaxis on surfaces. Nat Microbiol 9 , 2308–2322 (2024). https://doi.org/10.1038/s41564-024-01729-3

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Received : 02 August 2023

Accepted : 10 May 2024

Published : 02 September 2024

Issue Date : September 2024

DOI : https://doi.org/10.1038/s41564-024-01729-3

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