write an appropriate non directional hypothesis for this experiment

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

Nothing much! If the study is a laboratory experiment then we can call the hypothesis “an experimental hypothesis”, where we make a prediction about how the IV causes an effect on the DV. If we have a non-experimental design, i.e. we are not able to manipulate the IV as in a natural or quasi-experiment , or if some other research method has been used, then we call it an “alternativehypothesis”, 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 in the following 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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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., =).

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.

On This Page:

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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Directional and non-directional hypothesis: A Comprehensive Guide

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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 Hypothesis: Definition and 10 Examples

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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Aims And Hypotheses, Directional And Non-Directional

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

• Back to Paper 2 - 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).

It is important that the only variable that is changed in research is the  independent variable (IV),   all other variables have to be kept constant across the control condition and the experimental conditions. Only then will researchers be able to observe the true effects of  just  the independent variable (IV) on the dependent variable (DV).

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”.

It is important that aims created in research are realistic and ethical.

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.

Three Different Hypotheses:

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8.4: The Alternative Hypothesis

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• Page ID 14493

• Foster et al.
• University of Missouri-St. Louis, Rice University, & University of Houston, Downtown Campus via University of Missouri’s Affordable and Open Access Educational Resources Initiative

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If the null hypothesis is rejected, then we will need some other explanation, which we call the alternative hypothesis, \(H_A\) or \(H_1\). The alternative hypothesis is simply the reverse of the null hypothesis, and there are three options, depending on where we expect the difference to lie. Thus, our alternative hypothesis is the mathematical way of stating our research question. If we expect our obtained sample mean to be above or below the null hypothesis value, which we call a directional hypothesis, then our alternative hypothesis takes the form:

\[\mathrm{H}_{\mathrm{A}}: \mu>7.47 \quad \text { or } \quad \mathrm{H}_{\mathrm{A}}: \mu<7.47 \nonumber \]

based on the research question itself. We should only use a directional hypothesis if we have good reason, based on prior observations or research, to suspect a particular direction. When we do not know the direction, such as when we are entering a new area of research, we use a non-directional alternative:

\[\mathrm{H}_{\mathrm{A}}: \mu \neq 7.47 \nonumber \]

We will set different criteria for rejecting the null hypothesis based on the directionality (greater than, less than, or not equal to) of the alternative. To understand why, we need to see where our criteria come from and how they relate to \(z\)-scores and distributions.

Writing hypotheses in words

As we alluded to in the null hypothesis section, we can write our hypotheses in word statements (in addition to the statements with symbols). These statements should be specific enough to the particular experiment or situation being referred to. That is, don't make them generic enough so that they would apply to any hypothesis test that you would conduct. 

Examples for how to write null and alternate hypotheses in words for directional and non-directional situations are given throughout the chapters. 

Contributors and Attributions

Foster et al.  (University of Missouri-St. Louis, Rice University, & University of Houston, Downtown Campus)

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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.

Statistics Made Easy

What is a Directional Hypothesis? (Definition & Examples)

A statistical hypothesis is an assumption about a population parameter . For example, we may assume that the mean height of a male in the U.S. is 70 inches.

The assumption about the height is the statistical hypothesis and the true mean height of a male in the U.S. is the population parameter .

To test whether a statistical hypothesis about a population parameter is true, we obtain a random sample from the population and perform a hypothesis test on the sample data.

Whenever we perform a hypothesis test, we always write down a null and alternative hypothesis:

• Null Hypothesis (H 0 ): The sample data occurs purely from chance.
• Alternative Hypothesis (H A ): The sample data is influenced by some non-random cause.

A hypothesis test can either contain a directional hypothesis or a non-directional hypothesis:

• Directional hypothesis: The alternative hypothesis contains the less than (“<“) or greater than (“>”) sign. This indicates that we’re testing whether or not there is a positive or negative effect.
• Non-directional hypothesis: The alternative hypothesis contains the not equal (“≠”) sign. This indicates that we’re testing whether or not there is some effect, without specifying the direction of the effect.

Note that directional hypothesis tests are also called “one-tailed” tests and non-directional hypothesis tests are also called “two-tailed” tests.

Check out the following examples to gain a better understanding of directional vs. non-directional hypothesis tests.

Example 1: Baseball Programs

A baseball coach believes a certain 4-week program will increase the mean hitting percentage of his players, which is currently 0.285.

To test this, he measures the hitting percentage of each of his players before and after participating in the program.

He then performs a hypothesis test using the following hypotheses:

• H 0 : μ = .285 (the program will have no effect on the mean hitting percentage)
• H A : μ > .285 (the program will cause mean hitting percentage to increase)

This is an example of a directional hypothesis because the alternative hypothesis contains the greater than “>” sign. The coach believes that the program will influence the mean hitting percentage of his players in a positive direction.

Example 2: Plant Growth

A biologist believes that a certain pesticide will cause plants to grow less during a one-month period than they normally do, which is currently 10 inches.

To test this, she applies the pesticide to each of the plants in her laboratory for one month.

She then performs a hypothesis test using the following hypotheses:

• H 0 : μ = 10 inches (the pesticide will have no effect on the mean plant growth)
• H A : μ < 10 inches (the pesticide will cause mean plant growth to decrease)

This is also an example of a directional hypothesis because the alternative hypothesis contains the less than “<” sign. The biologist believes that the pesticide will influence the mean plant growth in a negative direction.

Example 3: Studying Technique

A professor believes that a certain studying technique will influence the mean score that her students receive on a certain exam, but she’s unsure if it will increase or decrease the mean score, which is currently 82.

To test this, she lets each student use the studying technique for one month leading up to the exam and then administers the same exam to each of the students.

• H 0 : μ = 82 (the studying technique will have no effect on the mean exam score)
• H A : μ ≠ 82 (the studying technique will cause the mean exam score to be different than 82)

This is an example of a non-directional hypothesis because the alternative hypothesis contains the not equal “≠” sign. The professor believes that the studying technique will influence the mean exam score, but doesn’t specify whether it will cause the mean score to increase or decrease.

Additional Resources

Introduction to Hypothesis Testing Introduction to the One Sample t-test Introduction to the Two Sample t-test Introduction to the Paired Samples t-test

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Hey there. My name is Zach Bobbitt. I have a Masters of Science degree in Applied Statistics and I’ve worked on machine learning algorithms for professional businesses in both healthcare and retail. I’m passionate about statistics, machine learning, and data visualization and I created Statology to be a resource for both students and teachers alike.  My goal with this site is to help you learn statistics through using simple terms, plenty of real-world examples, and helpful illustrations.

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Directional vs Non-Directional Hypothesis: Difference Between Them

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

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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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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.

In this blog post, we will explore the differences between Directional vs. Non-Directional Hypothesis in Research and their implications in research.

• Table of Contents

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.

The directional hypothesis is often used when there is prior knowledge or theoretical reasoning supporting the predicted direction of the relationship. It allows researchers to make more specific predictions and draw conclusions based on the expected direction of the effect.

Example of Directional Hypothesis

For example, a directional hypothesis might state that “increased physical activity will lead to a decrease in body weight.” Here, the researcher expects a negative relationship between physical activity and body weight.

Advantages of Directional Hypothesis

• Specific predictions: Directional hypotheses provide a clear prediction of the expected relationship between variables, allowing for a focused investigation.
• Increased statistical power: By focusing on one direction of the relationship, researchers can allocate more statistical power to that specific direction, increasing the chances of detecting a significant effect if it exists.

Non-Directional Hypothesis

A non-directional hypothesis, also known as a two-tailed hypothesis, does not make a specific prediction about the direction of the relationship between variables. Instead, it states that there is a relationship, but without indicating whether it will be positive or negative.

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 .

Read More: Internal Validity vs External Validity | Examples

Example of Non-Directional Hypothesis

For example, a non-directional hypothesis might state that “there is a relationship between caffeine consumption and reaction time.” Here, the researcher expects a relationship between the variables but does not specify the direction.

Read More: Population vs Sample | Examples

Advantages of Non-Directional Hypothesis:

• Flexibility: Non-directional hypotheses provide flexibility in exploring relationships between variables without preconceived notions about the direction of the effect.
• Open to unexpected findings : By not specifying the direction, researchers remain open to unexpected results or alternative explanations that may emerge during the analysis.

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:

• Prior research: If previous studies have established a clear direction of the relationship, a directional hypothesis may be more appropriate.
• Theoretical reasoning: If there is a strong theoretical foundation supporting a specific direction, a directional hypothesis can provide a focused investigation.
• Exploratory nature: If the research question is exploratory or lacks prior knowledge, a non-directional hypothesis allows for a more open-ended investigation.

Read More: Reliability vs Validity | Examples

• Directional vs. Non-Directional Hypothesis

Formulating hypotheses is an essential step in the research process, guiding researchers in testing relationships between variables.

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.

The choice between these types of hypotheses depends on the research question, prior knowledge, and theoretical background.

By understanding the distinctions between directional and non-directional hypotheses, researchers can effectively formulate hypotheses that align with their research goals and contribute to the advancement of scientific knowledge.

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.

Other articles

Please read through some of our other articles with examples and explanations if you’d like to learn more about research methodology.

Comparision

• Basic and Applied Research
• Cross-Sectional vs Longitudinal Studies
• Survey vs Questionnaire
• Open Ended vs Closed Ended Questions
• Experimental and Non-Experimental Research
• Inductive vs Deductive Approach
• Null and Alternative Hypothesis
• Reliability vs Validity
• Population vs Sample
• Conceptual Framework and Theoretical Framework
• Bibliography and Reference
• Stratified vs Cluster Sampling
• Sampling Error vs Sampling Bias
• Internal Validity vs External Validity
• Full-Scale, Laboratory-Scale and Pilot-Scale Studies
• Plagiarism and Paraphrasing
• Research Methodology Vs. Research Method
• Mediator and Moderator
• Type I vs Type II error
• Descriptive and Inferential Statistics
• Microsoft Excel and SPSS
• Parametric and Non-Parametric Test
• Independent vs. Dependent Variable – MIM Learnovate
• Research Article and Research Paper
• Proposition and Hypothesis
• Principal Component Analysis and Partial Least Squares
• Academic Research vs Industry Research
• Clinical Research vs Lab Research
• Research Lab and Hospital Lab
• Thesis Statement and Research Question
• Quantitative Researchers vs. Quantitative Traders
• Premise, Hypothesis and Supposition
• Survey Vs Experiment
• Hypothesis and Theory
• Independent vs. Dependent Variable
• APA vs. MLA
• Ghost Authorship vs. Gift Authorship
• Research Methods
• Quantitative Research
• Qualitative Research
• Case Study Research
• Survey Research
• Conclusive Research
• Descriptive Research
• Cross-Sectional Research
• Theoretical Framework
• Conceptual Framework
• Triangulation
• Grounded Theory
• Quasi-Experimental Design
• Mixed Method
• Correlational Research
• Randomized Controlled Trial
• Stratified Sampling
• Ethnography
• Ghost Authorship
• Secondary Data Collection
• Primary Data Collection
• Ex-Post-Facto
•   Dissertation Topic
• Thesis Statement
• Research Proposal
• Research Questions
• Research Problem
• Research Gap
• Types of Research Gaps
• Operationalization of Variables
• Literature Review
• Research Hypothesis
• Questionnaire
• Reliability
• Measurement of Scale
• Sampling Techniques
• Acknowledgements
• PLS-SEM model
• Principal Components Analysis
• Multivariate Analysis
• Friedman Test
• Chi-Square Test (Χ²)
• Effect Size

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Directional Hypothesis

Definition:

A directional hypothesis is a specific type of hypothesis statement in which the researcher predicts the direction or effect of the relationship between two variables.

Key Features

1. Predicts direction:

Unlike a non-directional hypothesis, which simply states that there is a relationship between two variables, a directional hypothesis specifies the expected direction of the relationship.

2. Involves one-tailed test:

Directional hypotheses typically require a one-tailed statistical test, as they are concerned with whether the relationship is positive or negative, rather than simply whether a relationship exists.

3. Example:

An example of a directional hypothesis would be: “Increasing levels of exercise will result in greater weight loss.”

4. Researcher’s prior belief:

A directional hypothesis is often formed based on the researcher’s prior knowledge, theoretical understanding, or previous empirical evidence relating to the variables under investigation.

5. Confirmatory nature:

Directional hypotheses are considered confirmatory, as they provide a specific prediction that can be tested statistically, allowing researchers to either support or reject the hypothesis.

6. Advantages and disadvantages:

Directional hypotheses help focus the research by explicitly stating the expected relationship, but they can also limit exploration of alternative explanations or unexpected findings.