hypothesis and testable predictions

What Is a Testable Hypothesis?

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A hypothesis is a tentative answer to a scientific question. A testable hypothesis is a  hypothesis that can be proved or disproved as a result of testing, data collection, or experience. Only testable hypotheses can be used to conceive and perform an experiment using the scientific method .

Requirements for a Testable Hypothesis

In order to be considered testable, two criteria must be met:

• It must be possible to prove that the hypothesis is true.
• It must be possible to prove that the hypothesis is false.
• It must be possible to reproduce the results of the hypothesis.

Examples of a Testable Hypothesis

All the following hypotheses are testable. It's important, however, to note that while it's possible to say that the hypothesis is correct, much more research would be required to answer the question " why is this hypothesis correct?" 

• Students who attend class have higher grades than students who skip class.  This is testable because it is possible to compare the grades of students who do and do not skip class and then analyze the resulting data. Another person could conduct the same research and come up with the same results.
• People exposed to high levels of ultraviolet light have a higher incidence of cancer than the norm.  This is testable because it is possible to find a group of people who have been exposed to high levels of ultraviolet light and compare their cancer rates to the average.
• If you put people in a dark room, then they will be unable to tell when an infrared light turns on.  This hypothesis is testable because it is possible to put a group of people into a dark room, turn on an infrared light, and ask the people in the room whether or not an infrared light has been turned on.

Examples of a Hypothesis Not Written in a Testable Form

• It doesn't matter whether or not you skip class.  This hypothesis can't be tested because it doesn't make any actual claim regarding the outcome of skipping class. "It doesn't matter" doesn't have any specific meaning, so it can't be tested.
• Ultraviolet light could cause cancer.  The word "could" makes a hypothesis extremely difficult to test because it is very vague. There "could," for example, be UFOs watching us at every moment, even though it's impossible to prove that they are there!
• Goldfish make better pets than guinea pigs.  This is not a hypothesis; it's a matter of opinion. There is no agreed-upon definition of what a "better" pet is, so while it is possible to argue the point, there is no way to prove it.

How to Propose a Testable Hypothesis

Now that you know what a testable hypothesis is, here are tips for proposing one.

• Try to write the hypothesis as an if-then statement. If you take an action, then a certain outcome is expected.
• Identify the independent and dependent variable in the hypothesis. The independent variable is what you are controlling or changing. You measure the effect this has on the dependent variable.
• Write the hypothesis in such a way that you can prove or disprove it. For example, a person has skin cancer, you can't prove they got it from being out in the sun. However, you can demonstrate a relationship between exposure to ultraviolet light and increased risk of skin cancer.
• Make sure you are proposing a hypothesis you can test with reproducible results. If your face breaks out, you can't prove the breakout was caused by the french fries you had for dinner last night. However, you can measure whether or not eating french fries is associated with breaking out. It's a matter of gathering enough data to be able to reproduce results and draw a conclusion.
• What Are Examples of a Hypothesis?
• What Are the Elements of a Good Hypothesis?
• What Is a Hypothesis? (Science)
• Understanding Simple vs Controlled Experiments
• How To Design a Science Fair Experiment
• Scientific Method Vocabulary Terms
• Null Hypothesis Definition and Examples
• Theory Definition in Science
• Hypothesis, Model, Theory, and Law
• Six Steps of the Scientific Method
• What 'Fail to Reject' Means in a Hypothesis Test
• Scientific Method Flow Chart
• Null Hypothesis Examples
• Definition of a Hypothesis
• What Is an Experiment? Definition and Design

Research Hypothesis In Psychology: Types, & Examples

Saul Mcleod, PhD

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

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

Some key points about hypotheses:

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

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

Types of Research Hypotheses

Alternative hypothesis.

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

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

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

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

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

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

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

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

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

Null Hypothesis

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

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

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

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

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

Nondirectional Hypothesis

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

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

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

Directional Hypothesis

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

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

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

Falsifiability

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

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

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

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

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

Can a Hypothesis be Proven?

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

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

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

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

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

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

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

How to Write a Hypothesis

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

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

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

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

More Examples

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

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Biology library

Course: biology library   >   unit 1, the scientific method.

• Controlled experiments
• The scientific method and experimental design

Introduction

• Make an observation.
• Ask a question.
• Form a hypothesis , or testable explanation.
• Make a prediction based on the hypothesis.
• Test the prediction.
• Iterate: use the results to make new hypotheses or predictions.

Scientific method example: Failure to toast

1. make an observation..

• Observation: the toaster won't toast.

2. Ask a question.

• Question: Why won't my toaster toast?

3. Propose a hypothesis.

• Hypothesis: Maybe the outlet is broken.

4. Make predictions.

• Prediction: If I plug the toaster into a different outlet, then it will toast the bread.

5. Test the predictions.

• Test of prediction: Plug the toaster into a different outlet and try again.
• If the toaster does toast, then the hypothesis is supported—likely correct.
• If the toaster doesn't toast, then the hypothesis is not supported—likely wrong.

Logical possibility

Practical possibility, building a body of evidence, 6. iterate..

• Iteration time!
• If the hypothesis was supported, we might do additional tests to confirm it, or revise it to be more specific. For instance, we might investigate why the outlet is broken.
• If the hypothesis was not supported, we would come up with a new hypothesis. For instance, the next hypothesis might be that there's a broken wire in the toaster.

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How to Write a Hypothesis: A Step-by-Step Guide

Introduction

An overview of the research hypothesis, different types of hypotheses, variables in a hypothesis, how to formulate an effective research hypothesis, designing a study around your hypothesis.

The scientific method can derive and test predictions as hypotheses. Empirical research can then provide support (or lack thereof) for the hypotheses. Even failure to find support for a hypothesis still represents a valuable contribution to scientific knowledge. Let's look more closely at the idea of the hypothesis and the role it plays in research.

As much as the term exists in everyday language, there is a detailed development that informs the word "hypothesis" when applied to research. A good research hypothesis is informed by prior research and guides research design and data analysis , so it is important to understand how a hypothesis is defined and understood by researchers.

What is the simple definition of a hypothesis?

A hypothesis is a testable prediction about an outcome between two or more variables . It functions as a navigational tool in the research process, directing what you aim to predict and how.

What is the hypothesis for in research?

In research, a hypothesis serves as the cornerstone for your empirical study. It not only lays out what you aim to investigate but also provides a structured approach for your data collection and analysis.

Essentially, it bridges the gap between the theoretical and the empirical, guiding your investigation throughout its course.

What is an example of a hypothesis?

If you are studying the relationship between physical exercise and mental health, a suitable hypothesis could be: "Regular physical exercise leads to improved mental well-being among adults."

This statement constitutes a specific and testable hypothesis that directly relates to the variables you are investigating.

What makes a good hypothesis?

A good hypothesis possesses several key characteristics. Firstly, it must be testable, allowing you to analyze data through empirical means, such as observation or experimentation, to assess if there is significant support for the hypothesis. Secondly, a hypothesis should be specific and unambiguous, giving a clear understanding of the expected relationship between variables. Lastly, it should be grounded in existing research or theoretical frameworks , ensuring its relevance and applicability.

Understanding the types of hypotheses can greatly enhance how you construct and work with hypotheses. While all hypotheses serve the essential function of guiding your study, there are varying purposes among the types of hypotheses. In addition, all hypotheses stand in contrast to the null hypothesis, or the assumption that there is no significant relationship between the variables .

Here, we explore various kinds of hypotheses to provide you with the tools needed to craft effective hypotheses for your specific research needs. Bear in mind that many of these hypothesis types may overlap with one another, and the specific type that is typically used will likely depend on the area of research and methodology you are following.

Null hypothesis

The null hypothesis is a statement that there is no effect or relationship between the variables being studied. In statistical terms, it serves as the default assumption that any observed differences are due to random chance.

For example, if you're studying the effect of a drug on blood pressure, the null hypothesis might state that the drug has no effect.

Alternative hypothesis

Contrary to the null hypothesis, the alternative hypothesis suggests that there is a significant relationship or effect between variables.

Using the drug example, the alternative hypothesis would posit that the drug does indeed affect blood pressure. This is what researchers aim to prove.

Simple hypothesis

A simple hypothesis makes a prediction about the relationship between two variables, and only two variables.

For example, "Increased study time results in better exam scores." Here, "study time" and "exam scores" are the only variables involved.

Complex hypothesis

A complex hypothesis, as the name suggests, involves more than two variables. For instance, "Increased study time and access to resources result in better exam scores." Here, "study time," "access to resources," and "exam scores" are all variables.

This hypothesis refers to multiple potential mediating variables. Other hypotheses could also include predictions about variables that moderate the relationship between the independent variable and dependent variable .

Directional hypothesis

A directional hypothesis specifies the direction of the expected relationship between variables. For example, "Eating more fruits and vegetables leads to a decrease in heart disease."

Here, the direction of heart disease is explicitly predicted to decrease, due to effects from eating more fruits and vegetables. All hypotheses typically specify the expected direction of the relationship between the independent and dependent variable, such that researchers can test if this prediction holds in their data analysis .

Statistical hypothesis

A statistical hypothesis is one that is testable through statistical methods, providing a numerical value that can be analyzed. This is commonly seen in quantitative research .

For example, "There is a statistically significant difference in test scores between students who study for one hour and those who study for two."

Empirical hypothesis

An empirical hypothesis is derived from observations and is tested through empirical methods, often through experimentation or survey data . Empirical hypotheses may also be assessed with statistical analyses.

For example, "Regular exercise is correlated with a lower incidence of depression," could be tested through surveys that measure exercise frequency and depression levels.

Causal hypothesis

A causal hypothesis proposes that one variable causes a change in another. This type of hypothesis is often tested through controlled experiments.

For example, "Smoking causes lung cancer," assumes a direct causal relationship.

Associative hypothesis

Unlike causal hypotheses, associative hypotheses suggest a relationship between variables but do not imply causation.

For instance, "People who smoke are more likely to get lung cancer," notes an association but doesn't claim that smoking causes lung cancer directly.

Relational hypothesis

A relational hypothesis explores the relationship between two or more variables but doesn't specify the nature of the relationship.

For example, "There is a relationship between diet and heart health," leaves the nature of the relationship (causal, associative, etc.) open to interpretation.

Logical hypothesis

A logical hypothesis is based on sound reasoning and logical principles. It's often used in theoretical research to explore abstract concepts, rather than being based on empirical data.

For example, "If all men are mortal and Socrates is a man, then Socrates is mortal," employs logical reasoning to make its point.

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In any research hypothesis, variables play a critical role. These are the elements or factors that the researcher manipulates, controls, or measures. Understanding variables is essential for crafting a clear, testable hypothesis and for the stages of research that follow, such as data collection and analysis.

In the realm of hypotheses, there are generally two types of variables to consider: independent and dependent. Independent variables are what you, as the researcher, manipulate or change in your study. It's considered the cause in the relationship you're investigating. For instance, in a study examining the impact of sleep duration on academic performance, the independent variable would be the amount of sleep participants get.

Conversely, the dependent variable is the outcome you measure to gauge the effect of your manipulation. It's the effect in the cause-and-effect relationship. The dependent variable thus refers to the main outcome of interest in your study. In the same sleep study example, the academic performance, perhaps measured by exam scores or GPA, would be the dependent variable.

Beyond these two primary types, you might also encounter control variables. These are variables that could potentially influence the outcome and are therefore kept constant to isolate the relationship between the independent and dependent variables . For example, in the sleep and academic performance study, control variables could include age, diet, or even the subject of study.

By clearly identifying and understanding the roles of these variables in your hypothesis, you set the stage for a methodologically sound research project. It helps you develop focused research questions, design appropriate experiments or observations, and carry out meaningful data analysis . It's a step that lays the groundwork for the success of your entire study.

Crafting a strong, testable hypothesis is crucial for the success of any research project. It sets the stage for everything from your study design to data collection and analysis . Below are some key considerations to keep in mind when formulating your hypothesis:

• Be specific : A vague hypothesis can lead to ambiguous results and interpretations . Clearly define your variables and the expected relationship between them.
• Ensure testability : A good hypothesis should be testable through empirical means, whether by observation , experimentation, or other forms of data analysis.
• Ground in literature : Before creating your hypothesis, consult existing research and theories. This not only helps you identify gaps in current knowledge but also gives you valuable context and credibility for crafting your hypothesis.
• Use simple language : While your hypothesis should be conceptually sound, it doesn't have to be complicated. Aim for clarity and simplicity in your wording.
• State direction, if applicable : If your hypothesis involves a directional outcome (e.g., "increase" or "decrease"), make sure to specify this. You also need to think about how you will measure whether or not the outcome moved in the direction you predicted.
• Keep it focused : One of the common pitfalls in hypothesis formulation is trying to answer too many questions at once. Keep your hypothesis focused on a specific issue or relationship.
• Account for control variables : Identify any variables that could potentially impact the outcome and consider how you will control for them in your study.
• Be ethical : Make sure your hypothesis and the methods for testing it comply with ethical standards , particularly if your research involves human or animal subjects.

Designing your study involves multiple key phases that help ensure the rigor and validity of your research. Here we discuss these crucial components in more detail.

Literature review

Starting with a comprehensive literature review is essential. This step allows you to understand the existing body of knowledge related to your hypothesis and helps you identify gaps that your research could fill. Your research should aim to contribute some novel understanding to existing literature, and your hypotheses can reflect this. A literature review also provides valuable insights into how similar research projects were executed, thereby helping you fine-tune your own approach.

Research methods

Choosing the right research methods is critical. Whether it's a survey, an experiment, or observational study, the methodology should be the most appropriate for testing your hypothesis. Your choice of methods will also depend on whether your research is quantitative, qualitative, or mixed-methods. Make sure the chosen methods align well with the variables you are studying and the type of data you need.

Preliminary research

Before diving into a full-scale study, it’s often beneficial to conduct preliminary research or a pilot study . This allows you to test your research methods on a smaller scale, refine your tools, and identify any potential issues. For instance, a pilot survey can help you determine if your questions are clear and if the survey effectively captures the data you need. This step can save you both time and resources in the long run.

Data analysis

Finally, planning your data analysis in advance is crucial for a successful study. Decide which statistical or analytical tools are most suited for your data type and research questions . For quantitative research, you might opt for t-tests, ANOVA, or regression analyses. For qualitative research , thematic analysis or grounded theory may be more appropriate. This phase is integral for interpreting your results and drawing meaningful conclusions in relation to your research question.

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Home » What is a Hypothesis – Types, Examples and Writing Guide

What is a Hypothesis – Types, Examples and Writing Guide

Table of Contents

Definition:

Hypothesis is an educated guess or proposed explanation for a phenomenon, based on some initial observations or data. It is a tentative statement that can be tested and potentially proven or disproven through further investigation and experimentation.

Hypothesis is often used in scientific research to guide the design of experiments and the collection and analysis of data. It is an essential element of the scientific method, as it allows researchers to make predictions about the outcome of their experiments and to test those predictions to determine their accuracy.

Types of Hypothesis

Types of Hypothesis are as follows:

Research Hypothesis

A research hypothesis is a statement that predicts a relationship between variables. It is usually formulated as a specific statement that can be tested through research, and it is often used in scientific research to guide the design of experiments.

Null Hypothesis

The null hypothesis is a statement that assumes there is no significant difference or relationship between variables. It is often used as a starting point for testing the research hypothesis, and if the results of the study reject the null hypothesis, it suggests that there is a significant difference or relationship between variables.

Alternative Hypothesis

An alternative hypothesis is a statement that assumes there is a significant difference or relationship between variables. It is often used as an alternative to the null hypothesis and is tested against the null hypothesis to determine which statement is more accurate.

Directional Hypothesis

A directional hypothesis is a statement that predicts the direction of the relationship between variables. For example, a researcher might predict that increasing the amount of exercise will result in a decrease in body weight.

Non-directional Hypothesis

A non-directional hypothesis is a statement that predicts the relationship between variables but does not specify the direction. For example, a researcher might predict that there is a relationship between the amount of exercise and body weight, but they do not specify whether increasing or decreasing exercise will affect body weight.

Statistical Hypothesis

A statistical hypothesis is a statement that assumes a particular statistical model or distribution for the data. It is often used in statistical analysis to test the significance of a particular result.

Composite Hypothesis

A composite hypothesis is a statement that assumes more than one condition or outcome. It can be divided into several sub-hypotheses, each of which represents a different possible outcome.

Empirical Hypothesis

An empirical hypothesis is a statement that is based on observed phenomena or data. It is often used in scientific research to develop theories or models that explain the observed phenomena.

Simple Hypothesis

A simple hypothesis is a statement that assumes only one outcome or condition. It is often used in scientific research to test a single variable or factor.

Complex Hypothesis

A complex hypothesis is a statement that assumes multiple outcomes or conditions. It is often used in scientific research to test the effects of multiple variables or factors on a particular outcome.

Applications of Hypothesis

Hypotheses are used in various fields to guide research and make predictions about the outcomes of experiments or observations. Here are some examples of how hypotheses are applied in different fields:

• Science : In scientific research, hypotheses are used to test the validity of theories and models that explain natural phenomena. For example, a hypothesis might be formulated to test the effects of a particular variable on a natural system, such as the effects of climate change on an ecosystem.
• Medicine : In medical research, hypotheses are used to test the effectiveness of treatments and therapies for specific conditions. For example, a hypothesis might be formulated to test the effects of a new drug on a particular disease.
• Psychology : In psychology, hypotheses are used to test theories and models of human behavior and cognition. For example, a hypothesis might be formulated to test the effects of a particular stimulus on the brain or behavior.
• Sociology : In sociology, hypotheses are used to test theories and models of social phenomena, such as the effects of social structures or institutions on human behavior. For example, a hypothesis might be formulated to test the effects of income inequality on crime rates.
• Business : In business research, hypotheses are used to test the validity of theories and models that explain business phenomena, such as consumer behavior or market trends. For example, a hypothesis might be formulated to test the effects of a new marketing campaign on consumer buying behavior.
• Engineering : In engineering, hypotheses are used to test the effectiveness of new technologies or designs. For example, a hypothesis might be formulated to test the efficiency of a new solar panel design.

How to write a Hypothesis

Here are the steps to follow when writing a hypothesis:

Identify the Research Question

The first step is to identify the research question that you want to answer through your study. This question should be clear, specific, and focused. It should be something that can be investigated empirically and that has some relevance or significance in the field.

Conduct a Literature Review

Before writing your hypothesis, it’s essential to conduct a thorough literature review to understand what is already known about the topic. This will help you to identify the research gap and formulate a hypothesis that builds on existing knowledge.

Determine the Variables

The next step is to identify the variables involved in the research question. A variable is any characteristic or factor that can vary or change. There are two types of variables: independent and dependent. The independent variable is the one that is manipulated or changed by the researcher, while the dependent variable is the one that is measured or observed as a result of the independent variable.

Formulate the Hypothesis

Based on the research question and the variables involved, you can now formulate your hypothesis. A hypothesis should be a clear and concise statement that predicts the relationship between the variables. It should be testable through empirical research and based on existing theory or evidence.

Write the Null Hypothesis

The null hypothesis is the opposite of the alternative hypothesis, which is the hypothesis that you are testing. The null hypothesis states that there is no significant difference or relationship between the variables. It is important to write the null hypothesis because it allows you to compare your results with what would be expected by chance.

Refine the Hypothesis

After formulating the hypothesis, it’s important to refine it and make it more precise. This may involve clarifying the variables, specifying the direction of the relationship, or making the hypothesis more testable.

Examples of Hypothesis

Here are a few examples of hypotheses in different fields:

• Psychology : “Increased exposure to violent video games leads to increased aggressive behavior in adolescents.”
• Biology : “Higher levels of carbon dioxide in the atmosphere will lead to increased plant growth.”
• Sociology : “Individuals who grow up in households with higher socioeconomic status will have higher levels of education and income as adults.”
• Education : “Implementing a new teaching method will result in higher student achievement scores.”
• Marketing : “Customers who receive a personalized email will be more likely to make a purchase than those who receive a generic email.”
• Physics : “An increase in temperature will cause an increase in the volume of a gas, assuming all other variables remain constant.”
• Medicine : “Consuming a diet high in saturated fats will increase the risk of developing heart disease.”

Purpose of Hypothesis

The purpose of a hypothesis is to provide a testable explanation for an observed phenomenon or a prediction of a future outcome based on existing knowledge or theories. A hypothesis is an essential part of the scientific method and helps to guide the research process by providing a clear focus for investigation. It enables scientists to design experiments or studies to gather evidence and data that can support or refute the proposed explanation or prediction.

The formulation of a hypothesis is based on existing knowledge, observations, and theories, and it should be specific, testable, and falsifiable. A specific hypothesis helps to define the research question, which is important in the research process as it guides the selection of an appropriate research design and methodology. Testability of the hypothesis means that it can be proven or disproven through empirical data collection and analysis. Falsifiability means that the hypothesis should be formulated in such a way that it can be proven wrong if it is incorrect.

In addition to guiding the research process, the testing of hypotheses can lead to new discoveries and advancements in scientific knowledge. When a hypothesis is supported by the data, it can be used to develop new theories or models to explain the observed phenomenon. When a hypothesis is not supported by the data, it can help to refine existing theories or prompt the development of new hypotheses to explain the phenomenon.

When to use Hypothesis

Here are some common situations in which hypotheses are used:

• In scientific research , hypotheses are used to guide the design of experiments and to help researchers make predictions about the outcomes of those experiments.
• In social science research , hypotheses are used to test theories about human behavior, social relationships, and other phenomena.
• I n business , hypotheses can be used to guide decisions about marketing, product development, and other areas. For example, a hypothesis might be that a new product will sell well in a particular market, and this hypothesis can be tested through market research.

Characteristics of Hypothesis

Here are some common characteristics of a hypothesis:

• Testable : A hypothesis must be able to be tested through observation or experimentation. This means that it must be possible to collect data that will either support or refute the hypothesis.
• Falsifiable : A hypothesis must be able to be proven false if it is not supported by the data. If a hypothesis cannot be falsified, then it is not a scientific hypothesis.
• Clear and concise : A hypothesis should be stated in a clear and concise manner so that it can be easily understood and tested.
• Based on existing knowledge : A hypothesis should be based on existing knowledge and research in the field. It should not be based on personal beliefs or opinions.
• Specific : A hypothesis should be specific in terms of the variables being tested and the predicted outcome. This will help to ensure that the research is focused and well-designed.
• Tentative: A hypothesis is a tentative statement or assumption that requires further testing and evidence to be confirmed or refuted. It is not a final conclusion or assertion.
• Relevant : A hypothesis should be relevant to the research question or problem being studied. It should address a gap in knowledge or provide a new perspective on the issue.

Advantages of Hypothesis

Hypotheses have several advantages in scientific research and experimentation:

• Guides research: A hypothesis provides a clear and specific direction for research. It helps to focus the research question, select appropriate methods and variables, and interpret the results.
• Predictive powe r: A hypothesis makes predictions about the outcome of research, which can be tested through experimentation. This allows researchers to evaluate the validity of the hypothesis and make new discoveries.
• Facilitates communication: A hypothesis provides a common language and framework for scientists to communicate with one another about their research. This helps to facilitate the exchange of ideas and promotes collaboration.
• Efficient use of resources: A hypothesis helps researchers to use their time, resources, and funding efficiently by directing them towards specific research questions and methods that are most likely to yield results.
• Provides a basis for further research: A hypothesis that is supported by data provides a basis for further research and exploration. It can lead to new hypotheses, theories, and discoveries.
• Increases objectivity: A hypothesis can help to increase objectivity in research by providing a clear and specific framework for testing and interpreting results. This can reduce bias and increase the reliability of research findings.

Limitations of Hypothesis

Some Limitations of the Hypothesis are as follows:

• Limited to observable phenomena: Hypotheses are limited to observable phenomena and cannot account for unobservable or intangible factors. This means that some research questions may not be amenable to hypothesis testing.
• May be inaccurate or incomplete: Hypotheses are based on existing knowledge and research, which may be incomplete or inaccurate. This can lead to flawed hypotheses and erroneous conclusions.
• May be biased: Hypotheses may be biased by the researcher’s own beliefs, values, or assumptions. This can lead to selective interpretation of data and a lack of objectivity in research.
• Cannot prove causation: A hypothesis can only show a correlation between variables, but it cannot prove causation. This requires further experimentation and analysis.
• Limited to specific contexts: Hypotheses are limited to specific contexts and may not be generalizable to other situations or populations. This means that results may not be applicable in other contexts or may require further testing.
• May be affected by chance : Hypotheses may be affected by chance or random variation, which can obscure or distort the true relationship between variables.

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How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Amy Morin, LCSW, is a psychotherapist and international bestselling author. Her books, including "13 Things Mentally Strong People Don't Do," have been translated into more than 40 languages. Her TEDx talk,  "The Secret of Becoming Mentally Strong," is one of the most viewed talks of all time.

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis.

• Operationalization

Hypothesis Types

Hypotheses examples.

• Collecting Data

A hypothesis is a tentative statement about the relationship between two or more variables. It is a specific, testable prediction about what you expect to happen in a study. It is a preliminary answer to your question that helps guide the research process.

Consider a study designed to examine the relationship between sleep deprivation and test performance. The hypothesis might be: "This study is designed to assess the hypothesis that sleep-deprived people will perform worse on a test than individuals who are not sleep-deprived."

At a Glance

A hypothesis is crucial to scientific research because it offers a clear direction for what the researchers are looking to find. This allows them to design experiments to test their predictions and add to our scientific knowledge about the world. This article explores how a hypothesis is used in psychology research, how to write a good hypothesis, and the different types of hypotheses you might use.

The Hypothesis in the Scientific Method

In the scientific method , whether it involves research in psychology, biology, or some other area, a hypothesis represents what the researchers think will happen in an experiment. The scientific method involves the following steps:

• Forming a question
• Performing background research
• Creating a hypothesis
• Designing an experiment
• Collecting data
• Analyzing the results
• Drawing conclusions
• Communicating the results

The hypothesis is a prediction, but it involves more than a guess. Most of the time, the hypothesis begins with a question which is then explored through background research. At this point, researchers then begin to develop a testable hypothesis.

Unless you are creating an exploratory study, your hypothesis should always explain what you  expect  to happen.

In a study exploring the effects of a particular drug, the hypothesis might be that researchers expect the drug to have some type of effect on the symptoms of a specific illness. In psychology, the hypothesis might focus on how a certain aspect of the environment might influence a particular behavior.

Remember, a hypothesis does not have to be correct. While the hypothesis predicts what the researchers expect to see, the goal of the research is to determine whether this guess is right or wrong. When conducting an experiment, researchers might explore numerous factors to determine which ones might contribute to the ultimate outcome.

In many cases, researchers may find that the results of an experiment  do not  support the original hypothesis. When writing up these results, the researchers might suggest other options that should be explored in future studies.

In many cases, researchers might draw a hypothesis from a specific theory or build on previous research. For example, prior research has shown that stress can impact the immune system. So a researcher might hypothesize: "People with high-stress levels will be more likely to contract a common cold after being exposed to the virus than people who have low-stress levels."

In other instances, researchers might look at commonly held beliefs or folk wisdom. "Birds of a feather flock together" is one example of folk adage that a psychologist might try to investigate. The researcher might pose a specific hypothesis that "People tend to select romantic partners who are similar to them in interests and educational level."

Elements of a Good Hypothesis

So how do you write a good hypothesis? When trying to come up with a hypothesis for your research or experiments, ask yourself the following questions:

• Is your hypothesis based on your research on a topic?
• Can your hypothesis be tested?
• Does your hypothesis include independent and dependent variables?

Before you come up with a specific hypothesis, spend some time doing background research. Once you have completed a literature review, start thinking about potential questions you still have. Pay attention to the discussion section in the  journal articles you read . Many authors will suggest questions that still need to be explored.

How to Formulate a Good Hypothesis

To form a hypothesis, you should take these steps:

• Collect as many observations about a topic or problem as you can.
• Evaluate these observations and look for possible causes of the problem.
• Create a list of possible explanations that you might want to explore.
• After you have developed some possible hypotheses, think of ways that you could confirm or disprove each hypothesis through experimentation. This is known as falsifiability.

In the scientific method ,  falsifiability is an important part of any valid hypothesis. In order to test a claim scientifically, it must be possible that the claim could be proven false.

Students sometimes confuse the idea of falsifiability with the idea that it means that something is false, which is not the case. What falsifiability means is that  if  something was false, then it is possible to demonstrate that it is false.

One of the hallmarks of pseudoscience is that it makes claims that cannot be refuted or proven false.

The Importance of Operational Definitions

A variable is a factor or element that can be changed and manipulated in ways that are observable and measurable. However, the researcher must also define how the variable will be manipulated and measured in the study.

Operational definitions are specific definitions for all relevant factors in a study. This process helps make vague or ambiguous concepts detailed and measurable.

For example, a researcher might operationally define the variable " test anxiety " as the results of a self-report measure of anxiety experienced during an exam. A "study habits" variable might be defined by the amount of studying that actually occurs as measured by time.

These precise descriptions are important because many things can be measured in various ways. Clearly defining these variables and how they are measured helps ensure that other researchers can replicate your results.

Replicability

One of the basic principles of any type of scientific research is that the results must be replicable.

Replication means repeating an experiment in the same way to produce the same results. By clearly detailing the specifics of how the variables were measured and manipulated, other researchers can better understand the results and repeat the study if needed.

Some variables are more difficult than others to define. For example, how would you operationally define a variable such as aggression ? For obvious ethical reasons, researchers cannot create a situation in which a person behaves aggressively toward others.

To measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming others. The researcher might utilize a simulated task to measure aggressiveness in this situation.

Hypothesis Checklist

• Does your hypothesis focus on something that you can actually test?
• Does your hypothesis include both an independent and dependent variable?
• Can you manipulate the variables?
• Can your hypothesis be tested without violating ethical standards?

The hypothesis you use will depend on what you are investigating and hoping to find. Some of the main types of hypotheses that you might use include:

• Simple hypothesis : This type of hypothesis suggests there is a relationship between one independent variable and one dependent variable.
• Complex hypothesis : This type suggests a relationship between three or more variables, such as two independent and dependent variables.
• Null hypothesis : This hypothesis suggests no relationship exists between two or more variables.
• Alternative hypothesis : This hypothesis states the opposite of the null hypothesis.
• Statistical hypothesis : This hypothesis uses statistical analysis to evaluate a representative population sample and then generalizes the findings to the larger group.
• Logical hypothesis : This hypothesis assumes a relationship between variables without collecting data or evidence.

A hypothesis often follows a basic format of "If {this happens} then {this will happen}." One way to structure your hypothesis is to describe what will happen to the  dependent variable  if you change the  independent variable .

The basic format might be: "If {these changes are made to a certain independent variable}, then we will observe {a change in a specific dependent variable}."

A few examples of simple hypotheses:

• "Students who eat breakfast will perform better on a math exam than students who do not eat breakfast."
• "Students who experience test anxiety before an English exam will get lower scores than students who do not experience test anxiety."​
• "Motorists who talk on the phone while driving will be more likely to make errors on a driving course than those who do not talk on the phone."
• "Children who receive a new reading intervention will have higher reading scores than students who do not receive the intervention."

Examples of a complex hypothesis include:

• "People with high-sugar diets and sedentary activity levels are more likely to develop depression."
• "Younger people who are regularly exposed to green, outdoor areas have better subjective well-being than older adults who have limited exposure to green spaces."

Examples of a null hypothesis include:

• "There is no difference in anxiety levels between people who take St. John's wort supplements and those who do not."
• "There is no difference in scores on a memory recall task between children and adults."
• "There is no difference in aggression levels between children who play first-person shooter games and those who do not."

Examples of an alternative hypothesis:

• "People who take St. John's wort supplements will have less anxiety than those who do not."
• "Adults will perform better on a memory task than children."
• "Children who play first-person shooter games will show higher levels of aggression than children who do not." 

Collecting Data on Your Hypothesis

Once a researcher has formed a testable hypothesis, the next step is to select a research design and start collecting data. The research method depends largely on exactly what they are studying. There are two basic types of research methods: descriptive research and experimental research.

Descriptive Research Methods

Descriptive research such as  case studies ,  naturalistic observations , and surveys are often used when  conducting an experiment is difficult or impossible. These methods are best used to describe different aspects of a behavior or psychological phenomenon.

Once a researcher has collected data using descriptive methods, a  correlational study  can examine how the variables are related. This research method might be used to investigate a hypothesis that is difficult to test experimentally.

Experimental Research Methods

Experimental methods  are used to demonstrate causal relationships between variables. In an experiment, the researcher systematically manipulates a variable of interest (known as the independent variable) and measures the effect on another variable (known as the dependent variable).

Unlike correlational studies, which can only be used to determine if there is a relationship between two variables, experimental methods can be used to determine the actual nature of the relationship—whether changes in one variable actually  cause  another to change.

The hypothesis is a critical part of any scientific exploration. It represents what researchers expect to find in a study or experiment. In situations where the hypothesis is unsupported by the research, the research still has value. Such research helps us better understand how different aspects of the natural world relate to one another. It also helps us develop new hypotheses that can then be tested in the future.

Thompson WH, Skau S. On the scope of scientific hypotheses .  R Soc Open Sci . 2023;10(8):230607. doi:10.1098/rsos.230607

Taran S, Adhikari NKJ, Fan E. Falsifiability in medicine: what clinicians can learn from Karl Popper [published correction appears in Intensive Care Med. 2021 Jun 17;:].  Intensive Care Med . 2021;47(9):1054-1056. doi:10.1007/s00134-021-06432-z

Eyler AA. Research Methods for Public Health . 1st ed. Springer Publishing Company; 2020. doi:10.1891/9780826182067.0004

Nosek BA, Errington TM. What is replication ?  PLoS Biol . 2020;18(3):e3000691. doi:10.1371/journal.pbio.3000691

Aggarwal R, Ranganathan P. Study designs: Part 2 - Descriptive studies .  Perspect Clin Res . 2019;10(1):34-36. doi:10.4103/picr.PICR_154_18

Nevid J. Psychology: Concepts and Applications. Wadworth, 2013.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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Hulley SB, Cummings SR, Browner WS, Grady DG, Newman TB. Designing clinical research. 3rd ed. Philadelphia: Lippincott Williams and Wilkins; 2007.

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Brian Haynes R. Forming research questions. J Clin Epidemiol. 2006;59(9):881–6.

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Farrugia P, Petrisor BA, Farrokhyar F, Bhandari M. Practical tips for surgical research: research questions, hypotheses and objectives. Can J Surg. 2010;53(4):278–81.

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Friedman LM, Furberg CD, DeMets DL, Reboussin DM, Granger CB. Fundamentals of clinical trials. 5th ed. New York, NY: Springer; 2015.

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Freedman B. Equipoise and the ethics of clinical research. N Engl J Med. 1987;317(3):141–5.

Nottage M, Siu LL. Principles of clinical trial design. J Clin Oncol. 2002;20(18 Suppl):42S–6S.

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Bland JM, Altman DG. One and two sided tests of significance. BMJ. 1994;309(6949):248.

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What Is a Testable Prediction?

What Are the 8 Steps in Scientific Research?

In science, an educated guess about the cause of a natural phenomenon is called a hypothesis. It's essential that hypotheses be testable and falsifiable, meaning they can be tested and different results will ensue depending on whether the hypothesis is true or false. In other words, a hypothesis should make predictions that will hold true if the hypothesis itself is true. A testable prediction can be verified through experiment.

If you have an explanation for a natural phenomenon -- in other words, a hypothesis -- you can use it to make predictions. Suppose you notice, for example, that more salt dissolves in hot water than in cold water. You could hypothesize that perhaps all compounds are more soluble in hot solvents than in cold solvents. Based on this hypothesis, you would predict that as the temperature of the solvent increases, so, too, does the amount of solute you can dissolve.

Testing Predictions

All predictions should be testable, meaning it should be possible to design an experiment that would verify or invalidate the prediction. With the solvent, for example, you could test your prediction by dissolving different compounds in water at different temperatures and measuring the solubility. You would soon find that some substances actually become less soluble with increasing temperature. Since the prediction made by your hypothesis is false, you would realize your hypothesis is flawed and try to find a new one that could account for the facts.

Untestable Predictions

Untestable predictions and hypotheses lie outside the realm of science. Suppose someone told you, for example, that lightning storms are caused by angry ghosts. If this is true, you would predict that when ghosts are angry, there will be more lightning storms. It's not a valid scientific hypothesis, however, because neither the proposed explanation nor its predictions are testable. There is no possible experiment you can design to determine whether ghosts are angry and whether their wrath is correlated with the incidence of thunderstorms, so the hypothesis and its predictions are completely untestable.

There's a common misconception that scientists "prove" a hypothesis is true. In reality, no number of experiments can ever prove a hypothesis is true beyond all doubt; they can only show it's consistent with the evidence. As evidence accumulates and competing explanations are disproven, of course, it becomes more and more reasonable to believe the hypothesis is the best explanation. At this point scientists will refer to it as a theory (for example, the theory of relativity). It takes only a single experiment to disprove a theory, but a thousand experiments cannot prove it true. Nonetheless, if a theory and its predictions have been repeatedly verified by experiment, it will be generally accepted, unless there is sufficient evidence to show it should be discarded in favor of a new theory.

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How To Write A Hypotheses – Guide For Students

The word “hypothesis” might conjure up images of scientists in white coats, but crafting a solid hypothesis is a crucial skill for students in any field. Whether you are analyzing Shakespeare’s sonnets or conducting a science experiment, a well-defined research hypothesis sets the stage for your dissertation or thesis and fuels your investigation. 

Table of Contents

Writing a hypothesis is a crucial step in the research process. A hypothesis serves as the foundation of your research paper because it guides the direction of your study and provides a clear framework for investigation. But how to write a hypothesis? This blog will help you craft one. Let’s get started.

What Is A Hypothesis

A hypothesis is a clear and testable thesis statement or prediction that serves as the foundation of a research study. It is formulated based on existing knowledge, observations, and theoretical frameworks. 

A hypothesis articulates the researcher’s expectations regarding the relationship between variables in a study.

Hypothesis Example

Students exposed to multimedia-enhanced teaching methods will demonstrate higher retention of information compared to those taught using traditional methods.

The formulation of a hypothesis is crucial for guiding the research process and providing a clear direction for data collection and analysis. A well-crafted research hypothesis not only makes the research purpose explicit but also sets the stage for drawing meaningful conclusions from the study’s findings.

What Is A Null Hypothesis And Alternative Hypothesis

There are two main types of hypotheses: the null hypothesis (H0) and the alternative hypothesis (H1 or Ha). 

The null hypothesis posits that there is no significant effect or relationship, while the alternative hypothesis suggests the presence of a significant effect or relationship.

For example, in a study investigating the effect of a new drug on blood pressure, the null hypothesis might state that there is no difference in blood pressure between the control group (not receiving the drug) and the experimental group (receiving the drug). The alternative hypothesis, on the other hand, would propose that there is a significant difference in blood pressure between the two groups.

The literature review we write have:

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How To Write A Good Research Hypothesis

Writing a hypothesis involves a systematic process that guides your research and provides a clear and testable statement about the expected relationship between variables. Go through the MLA vs. APA guidelines before writing. Here are the steps to help you how to write a hypothesis:

Step 1: Identify The Research Topic

Clearly define the research topic or question that you want to investigate. Ensure that your research question is specific and focused, providing a clear direction for your study.

Step 2: Conduct A Literature Review

Review existing literature related to your research topic. A thorough literature review helps you understand what is already known in the field, identify gaps, and build a foundation for formulating your hypothesis.

Step 3: Define Variables

Identify the variables involved in your study. The independent variable is the factor you manipulate, and the dependent variable is the one you measure. Clearly define the characteristics or conditions you are studying.

Step 4: Establish The Relationship

Determine the expected relationship between the independent and dependent variables. Will a change in the independent variable lead to a change in the dependent variable? Specify whether you anticipate a positive, negative, or no relationship.

Step 5: Formulate The Null Hypothesis (H0)

The null hypothesis represents the default position, suggesting that there is no significant effect or relationship between the variables you are studying. It serves as the baseline to be tested against. The null hypothesis is often denoted as H0.

Step 6: Formulate The Alternative Hypothesis (H1 or Ha)

The alternative hypothesis articulates the researcher’s expectation about the existence of a significant effect or relationship. It is what you aim to support with your research paper . The alternative hypothesis is denoted as H1 or Ha.

For example, if your research topic is about the effect of a new fertilizer on plant growth:

• Null Hypothesis (H0): There is no significant difference in plant growth between plants treated with the traditional fertilizer and those treated with the new fertilizer.
• Alternative Hypothesis (H1): There is a significant difference in plant growth between plants treated with the traditional fertilizer and those treated with the new fertilizer.

Step 7: Ensure Testability And Specificity

Confirm that your research hypothesis is testable and can be empirically investigated. Ensure that it is specific, providing a clear and measurable statement that can be validated or refuted through data collection and analysis.

Hypothesis Examples

What makes a good hypothesis.

• Clear Statement: A hypothesis should be stated clearly and precisely. It should be easily understandable and convey the expected relationship between variables.
• Testability: A hypothesis must be testable through empirical observation or experimentation. This means that there should be a feasible way to collect data and assess whether the expected relationship holds true.
• Specificity: The research hypothesis should be specific in terms of the variables involved and the nature of the expected relationship. Vague or ambiguous hypotheses can lead to unclear research outcomes.
• Measurability: Variables in a hypothesis should be measurable, meaning they can be quantified or observed objectively. This ensures that the research can be conducted with precision.
• Falsifiability: A good research hypothesis should be falsifiable, meaning there should be a possibility of proving it wrong. This concept is fundamental to the scientific method, as hypotheses that cannot be tested or disproven lack scientific validity.

Frequently Asked Questions

How to write a hypothesis.

• Clearly state the research question.
• Identify the variables involved.
• Formulate a clear and testable prediction.
• Use specific and measurable terms.
• Align the hypothesis with the research question.
• Distinguish between the null hypothesis (no effect) and alternative hypothesis (expected effect).
• Ensure the hypothesis is falsifiable and subject to empirical testing.

How to write a hypothesis for a lab?

• Identify the purpose of the lab.
• Clearly state the relationship between variables.
• Use concise language and specific terms.
• Make the hypothesis testable through experimentation.
• Align with the lab’s objectives.
• Include an if-then statement to express the expected outcome.
• Ensure clarity and relevance to the experimental setup.

What Is A Null Hypothesis?

A null hypothesis is a statement suggesting no effect or relationship between variables in a research study. It serves as the default assumption, stating that any observed differences or effects are due to chance. Researchers aim to reject the null hypothesis based on statistical evidence to support their alternative hypothesis.

How to write a null hypothesis?

• State there is no effect, difference, or relationship between variables.
• Use clear and specific language.
• Frame it in a testable manner.
• Align with the research question.
• Specify parameters for statistical testing.
• Consider it as the default assumption to be tested and potentially rejected in favour of the alternative hypothesis.

What is the p-value of a hypothesis test?

The p-value in a hypothesis test represents the probability of obtaining observed results, or more extreme ones, if the null hypothesis is true. A lower p-value suggests stronger evidence against the null hypothesis, often leading to its rejection. Common significance thresholds include 0.05 or 0.01.

How to write a hypothesis in science?

• Clearly state the research question
• Identify the variables and their relationship.
• Formulate a testable and falsifiable prediction.
• Use specific, measurable terms.
• Distinguish between the null and alternative hypotheses.
• Ensure clarity and relevance to the scientific investigation.

How to write a hypothesis for a research proposal?

• Clearly define the research question.
• Identify variables and their expected relationship.
• Formulate a specific, testable hypothesis.
• Align the hypothesis with the proposal’s objectives.
• Clearly articulate the null hypothesis.
• Use concise language and measurable terms.
• Ensure the hypothesis aligns with the proposed research methodology.

How to write a good hypothesis psychology?

• Formulate a specific and testable prediction.
• Use precise and measurable terms.
• Align the hypothesis with psychological theories.
• Articulate the null hypothesis.
• Ensure the hypothesis guides empirical testing in psychological research.

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Hypothesis vs. Prediction

What's the difference.

Hypothesis and prediction are both important components of the scientific method, but they serve different purposes. A hypothesis is a proposed explanation or statement that can be tested through experimentation or observation. It is based on prior knowledge, observations, or theories and is used to guide scientific research. On the other hand, a prediction is a specific statement about what will happen in a particular situation or experiment. It is often derived from a hypothesis and serves as a testable outcome that can be confirmed or refuted through data analysis. While a hypothesis provides a broader framework for scientific inquiry, a prediction is a more specific and measurable expectation of the results.

Further Detail

Introduction.

When it comes to scientific research and inquiry, two important concepts that often come into play are hypothesis and prediction. Both of these terms are used to make educated guesses or assumptions about the outcome of an experiment or study. While they share some similarities, they also have distinct attributes that set them apart. In this article, we will explore the characteristics of hypothesis and prediction, highlighting their differences and similarities.

A hypothesis is a proposed explanation or statement that can be tested through experimentation or observation. It is typically formulated based on existing knowledge, observations, or theories. A hypothesis is often used as a starting point for scientific research, as it provides a framework for investigation and helps guide the research process.

One of the key attributes of a hypothesis is that it is testable. This means that it can be subjected to empirical evidence and observations to determine its validity. A hypothesis should be specific and measurable, allowing researchers to design experiments or gather data to either support or refute the hypothesis.

Another important aspect of a hypothesis is that it is falsifiable. This means that it is possible to prove the hypothesis wrong through experimentation or observation. Falsifiability is crucial in scientific research, as it ensures that hypotheses can be objectively tested and evaluated.

Hypotheses can be classified into two main types: null hypotheses and alternative hypotheses. A null hypothesis states that there is no significant relationship or difference between variables, while an alternative hypothesis proposes the existence of a relationship or difference. These two types of hypotheses are often used in statistical analysis to draw conclusions from data.

In summary, a hypothesis is a testable and falsifiable statement that serves as a starting point for scientific research. It is specific, measurable, and can be either a null or alternative hypothesis.

While a hypothesis is a proposed explanation or statement, a prediction is a specific outcome or result that is anticipated based on existing knowledge or theories. Predictions are often made before conducting an experiment or study and serve as a way to anticipate the expected outcome.

Unlike a hypothesis, a prediction is not necessarily testable or falsifiable on its own. Instead, it is used to guide the research process and provide a basis for comparison with the actual results obtained from the experiment or study. Predictions can be based on previous research, theoretical models, or logical reasoning.

One of the key attributes of a prediction is that it is specific and precise. It should clearly state the expected outcome or result, leaving little room for ambiguity. This allows researchers to compare the prediction with the actual results and evaluate the accuracy of their anticipated outcome.

Predictions can also be used to generate hypotheses. By making a prediction and comparing it with the actual results, researchers can identify discrepancies or unexpected findings. These observations can then be used to formulate new hypotheses and guide further research.

In summary, a prediction is a specific anticipated outcome or result that is not necessarily testable or falsifiable on its own. It serves as a basis for comparison with the actual results obtained from an experiment or study and can be used to generate new hypotheses.

Similarities

While hypotheses and predictions have distinct attributes, they also share some similarities in the context of scientific research. Both hypotheses and predictions are based on existing knowledge, observations, or theories. They are both used to make educated guesses or assumptions about the outcome of an experiment or study.

Furthermore, both hypotheses and predictions play a crucial role in the scientific method. They provide a framework for research, guiding the design of experiments, data collection, and analysis. Both hypotheses and predictions are subject to evaluation and revision based on empirical evidence and observations.

Additionally, both hypotheses and predictions can be used to generate new knowledge and advance scientific understanding. By testing hypotheses and comparing predictions with actual results, researchers can gain insights into the relationships between variables, uncover new phenomena, or challenge existing theories.

Overall, while hypotheses and predictions have their own unique attributes, they are both integral components of scientific research and inquiry.

In conclusion, hypotheses and predictions are important concepts in scientific research. While a hypothesis is a testable and falsifiable statement that serves as a starting point for investigation, a prediction is a specific anticipated outcome or result that guides the research process. Hypotheses are specific, measurable, and can be either null or alternative, while predictions are precise and serve as a basis for comparison with actual results.

Despite their differences, hypotheses and predictions share similarities in terms of their reliance on existing knowledge, their role in the scientific method, and their potential to generate new knowledge. Both hypotheses and predictions contribute to the advancement of scientific understanding and play a crucial role in the research process.

By understanding the attributes of hypotheses and predictions, researchers can effectively formulate research questions, design experiments, and analyze data. These concepts are fundamental to the scientific method and are essential for the progress of scientific research and inquiry.

Comparisons may contain inaccurate information about people, places, or facts. Please report any issues.

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Science Struck

What’s the Real Difference Between Hypothesis and Prediction

Both hypothesis and prediction fall in the realm of guesswork, but with different assumptions. This Buzzle write-up below will elaborate on the differences between hypothesis and prediction.

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“There is no justifiable prediction about how the hypothesis will hold up in the future; its degree of corroboration simply is a historical statement describing how severely the hypothesis has been tested in the past.” ― Robert Nozick, American author, professor, and philosopher

A lot of people tend to think that a hypothesis is the same as prediction, but this is not true. They are entirely different terms, though they can be manifested within the same example. They are both entities that stem from statistics, and are used in a variety of applications like finance, mathematics, science (widely), sports, psychology, etc. A hypothesis may be a prediction, but the reverse may not be true.

Also, a prediction may or may not agree with the hypothesis. Confused? Don’t worry, read the hypothesis vs. prediction comparison, provided below with examples, to clear your doubts regarding both these entities.

• A hypothesis is a kind of guess or proposition regarding a situation.
• It can be called a kind of intelligent guess or prediction, and it needs to be proved using different methods.
• Formulating a hypothesis is an important step in experimental design, for it helps to predict things that might take place in the course of research.
• The strength of the statement is based on how effectively it is proved while conducting experiments.
• It is usually written in the ‘If-then-because’ format.
• For example, ‘ If Susan’s mood depends on the weather, then she will be happy today, because it is bright and sunny outside. ‘. Here, Susan’s mood is the dependent variable, and the weather is the independent variable. Thus, a hypothesis helps establish a relationship.
• A prediction is also a type of guess, in fact, it is a guesswork in the true sense of the word.
• It is not an educated guess, like a hypothesis, i.e., it is based on established facts.
• While making a prediction for various applications, you have to take into account all the current observations.
• It can be testable, but just once. This goes to prove that the strength of the statement is based on whether the predicted event occurs or not.
• It is harder to define, and it contains many variations, which is why, probably, it is confused to be a fictional guess or forecast.
• For example, He is studying very hard, he might score an A . Here, we are predicting that since the student is working hard, he might score good marks. It is based on an observation and does not establish any relationship.

Factors of Differentiation

♦ Consider a statement, ‘If I add some chili powder, the pasta may become spicy’. This is a hypothesis, and a testable statement. You can carry on adding 1 pinch of chili powder, or a spoon, or two spoons, and so on. The dish may become spicier or pungent, or there may be no reaction at all. The sum and substance is that, the amount of chili powder is the independent variable here, and the pasta dish is the dependent variable, which is expected to change with the addition of chili powder. This statement thus establishes and analyzes the relationship between both variables, and you will get a variety of results when the test is performed multiple times. Your hypothesis may even be opposed tomorrow.

♦ Consider the statement, ‘Robert has longer legs, he may run faster’. This is just a prediction. You may have read somewhere that people with long legs tend to run faster. It may or may not be true. What is important here is ‘Robert’. You are talking only of Robert’s legs, so you will test if he runs faster. If he does, your prediction is true, if he doesn’t, your prediction is false. No more testing.

♦ Consider a statement, ‘If you eat chocolates, you may get acne’. This is a simple hypothesis, based on facts, yet necessary to be proven. It can be tested on a number of people. It may be true, it may be false. The fact is, it defines a relationship between chocolates and acne. The relationship can be analyzed and the results can be recorded. Tomorrow, someone might come up with an alternative hypothesis that chocolate does not cause acne. This will need to be tested again, and so on. A hypothesis is thus, something that you think happens due to a reason.

♦ Consider a statement, ‘The sky is overcast, it may rain today’. A simple guess, based on the fact that it generally rains if the sky is overcast. It may not even be testable, i.e., the sky can be overcast now and clear the next minute. If it does rain, you have predicted correctly. If it does not, you are wrong. No further analysis or questions.

Both hypothesis and prediction need to be effectively structured so that further analysis of the problem statement is easier. Remember that, the key difference between the two is the procedure of proving the statements. Also, you cannot state one is better than the other, this depends entirely on the application in hand.

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The importance of making testable predictions: A cautionary tale

Emma s. choi.

Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, United States of America

Erik Saberski

Tom lorimer, cameron smith, unduwap kandage-don, ronald s. burton, george sugihara, associated data.

Data are available on GitHub ( https://github.com/SugiharaLab/Projects/tree/master/Spring%20Temperature%20Triggers ).

We found a startling correlation (Pearson ρ > 0.97) between a single event in daily sea surface temperatures each spring, and peak fish egg abundance measurements the following summer, in 7 years of approximately weekly fish egg abundance data collected at Scripps Pier in La Jolla California. Even more surprising was that this event-based result persisted despite the large and variable number of fish species involved (up to 46), and the large and variable time interval between trigger and response (up to ~3 months). To mitigate potential over-fitting, we made an out-of-sample prediction beyond the publication process for the peak summer egg abundance observed at Scripps Pier in 2020 (available on bioRxiv). During peer-review, the prediction failed, and while it would be tempting to explain this away as a result of the record-breaking toxic algal bloom that occurred during the spring (9x higher concentration of dinoflagellates than ever previously recorded), a re-examination of our methodology revealed a potential source of over-fitting that had not been evaluated for robustness. This cautionary tale highlights the importance of testable true out-of-sample predictions of future values that cannot (even accidentally) be used in model fitting, and that can therefore catch model assumptions that may otherwise escape notice. We believe that this example can benefit the current push towards ecology as a predictive science and support the notion that predictions should live and die in the public domain, along with the models that made them.

Introduction

To comprehend the population dynamics underpinning biodiversity and essential ecosystem services, a heavy emphasis is placed on driving mechanisms, both biotic and abiotic. In marine environments, where fish stocks are of substantial ecological and economic interest, drivers need to be untangled to inform effective, practical, and sustainable environmental policy. Temperature is a particularly important (and sometimes controversial [ 1 – 3 ]) driver for fish and other marine ectotherm populations. Here we focus on the well-studied relationship between temperature and fish reproduction [ 4 – 6 ].

At the seasonal timescale, trends between water temperature and spawning activity have been observed in many fish species [ 7 – 16 ]. Huber and Bengtson [ 11 ] found that the gonads of inland silversides ( Menidia beryllina ), a summer spawning species, did not mature to a reproductive level in the absence of increasing water temperatures. In yellow perch ( Perca flavenscens ) both decreasing autumn temperatures and low winter temperatures have been deemed critical in order for gonadal maturation to occur [ 12 , 13 ]. Additionally, these fish have been manipulated to spawn earlier in the year by increasing the rate of water temperature change [ 13 ]. Kayes and Calbert [ 14 ] found that for the same yellow perch species, increasing temperature heightened egg production, but even in the absence of a temperature cue endogenous factors could induce spawning. In cyprinid fishes, the initiation of gametogenesis requires low temperatures, but the completion of the process requires increasing temperatures [ 7 ]. Other notable studies use degree days, a measure of time based on temperature, to track gonad development from the initiation of vitellogenesis to the onset of spawning [ 17 , 18 ]. A study done by Henderson et al. [ 19 ] demonstrates that the timing of spring transitions and the duration of summer, defined by a temperature threshold, is related to shifts in the center of biomass for multiple species during their seasonal migration to spawning grounds, however, the shifts observed differ by species. These and other varying and apparently complicated specific effects suggest that more general quantitative relationships covering diverse species may be hard to come by.

Despite this, recently a strong quantitative predictive relationship was detected between average winter temperature and average spring-summer egg abundance for a suite of near-shore-spawning species off the coast of southern California [ 15 ], which was subsequently supported by out-of-sample data acquired the following year [ 16 ]. This relationship is largely explained by colder waters being indicative of large scale upwelling; a process known to supply nutrients to shallower waters [ 20 ].

Building on this encouraging result, we re-examined the data of [ 15 ] and [ 16 ], but now including the additional 2019 and 2020 data that have since become available, and found a true out-of-sample confirmation of that relationship ( Fig 1C ). To emphasize, that means the original relationship of Ref. [ 15 ] which was based on just five years of available data, has reasonably predicted the subsequent three years of egg abundance. The data underlying this relationship, which consist of approximately weekly-sampled, species-identified egg counts of 46 near-shore-spawning species from Scripps Pier since 2013 (see Methods ) also contain substantial, and possibly important, fine-timescale information that was not considered in the seasonal relationship described in [ 15 ]. Though the statistical seasonal association is compelling, it emerges only in large-scale averages. By coupling the full-resolution fish egg abundance time series ( Fig 1A ) with daily-averaged sea-surface temperatures ( Fig 1B ) from the Southern Californian Coastal Observational Ocean Monitoring System (SCCOOS) dataset, we asked whether finer-timescale temperature dynamics provide information about finer-timescale fish egg abundance dynamics.

A) The total egg abundance in each collection (see Methods ) shows substantial variability from year to year in both mean and peak levels. Each fish egg collection was made from the Scripps Institution of Oceanography (SIO) Pier. B) The daily averaged sea surface temperature (SST) in °C at the SIO Pier from data taken every 5–10 minutes from the SCCOOS monitoring station. C) Seasonal averaging reveals the strong negative correlation between the average winter (December–February) SST and the average spring and summer (March–August) egg abundance, identified by [ 15 ], with additional points for 2018 [ 16 ], and now 2019 and 2020. D) The seasonal correlation breaks down at the daily level; there is no similarly strong correlation between daily winter temperatures and daily egg abundances with time delays ranging from 0 to 180 days. E) The S-Map [ 21 ] test for nonlinearity shows that forecasts of egg abundance improve (correlation between predictions and observations) as the nonlinearity parameter (θ) is increased, indicating that egg abundance shows nonlinear behavior. F) Convergent cross-mapping (CCM, [ 22 ]), shows that when using the egg abundance time series to map onto the temperature time series, predictions improve as library size increases, indicating there is a dynamic causal effect of daily-averaged temperature on egg abundance.

The high time-resolution data ( Fig 1A and 1B ) did not show a linear cross-correlation between the daily spring temperature and lagged daily egg abundance, with only weak relationships across all delays at this fine daily timescale ( Fig 1D ). However, in accordance with previous work [ 23 – 26 ], the S-Map test for nonlinearity [ 21 ] revealed that the egg abundance is driven by nonlinear processes (forecasts improve as the nonlinear parameter, θ, is increased, Fig 1E ). Further, convergent cross-mapping, a tool for detecting nonlinear coupling in dynamical systems [ 22 ] suggested that temperature has a nonlinear effect on egg abundance (converges to ρ = 0.58, n = 295, Fig 1F ). Thus, we expected that a daily timescale relationship may be detectable, just not with linear correlation.

One type of event that stands out in the egg abundance time series ( Fig 1A ) is the peak in summer egg abundance. Both the magnitude and timing of the peak egg abundance varies from year to year with no obvious pattern. Previous studies indicate that increasing water temperature may provide a cue for spring and summer spawning species [ 19 , 27 – 29 ]. To ascertain whether a relationship exists between spring temperature increase and peak summer egg abundance, we defined a generic spring temperature trigger (STT). Our STT is the maximum of all temperature increases detected within a moving window of length L, as that window moves over the spring season ( Fig 2A , see Methods ). This returns a single scalar value for the season, corresponding to a single event with an interpretable characteristic timescale (L). We restricted our analysis of temperature to the spring season (i.e. the season preceding the summer peak) following roughly the causal timescale examined in [ 15 ]. By examining a range of possible window lengths, we found a robust relationship around the 1 month timescale, between STT and peak summer egg abundance (L between 3 and 5 weeks, ρ > 0.95, Fig 2B ). This relationship was so remarkably strong (ρ up to 0.98; Fig 2C ), and apparently robust ( Fig 2B ) that we felt compelled to share this observation, despite the small number of data points involved (n = 7). To mitigate potential overfitting, we offered a prediction for the 2020 peak summer egg abundance that at the time of writing had not yet been measured ( Fig 2C ) [ 30 ]. To examine whether this relationship was caused by a general spring warming trend, we repeated the analysis on increasingly smoothed (time averaged) temperature data. We found that the predictive relationship from STT to peak summer egg abundance decreased markedly as temperature data became increasingly smoothed ( Fig 3 ), which suggested to us that the information was indeed contained in the daily-resolution temperature information, and not in the trend.

A) We defined the spring temperature trigger (STT) as the largest temperature increase (denoted in red) detected within a monthly sliding window (gray area) as it moves in daily increments over the spring season (dashed lines; see Methods ). B) The relationship between STT and peak summer eggs was robust to the width of the sliding window (widths that produce a ρ > 0.95 for the data up to and including 2019 are indicated in green). C) The peak correlation between STT and peak summer eggs (June–August) for 2013–2019 (black dots) and predicted value of 801 eggs for 2020 based on the linear regression (red dot), which differs dramatically from the eventually observed peak summer eggs (blue dot).

A) The relationship between spring temperature trigger (STT defined over 27 days) and maximum summer fish egg abundance declined as SST was increasingly smoothed across the x-axis (from daily to monthly averages). B) The daily (blue), weekly averaged (purple), and monthly averaged (orange) sea-surface temperature in 2017. Note how the magnitude of the STT (red bars) declines with averaging. Note that this figure does not include 2020 data.

The failure of our published out-of-sample prediction for 2020 [ 30 ] naturally led us to ask whether an exogenous change in conditions had come into play. Indeed, 2020 has been an anomalous year in many ways, and for marine life at Scripps Pier it was most notably seen in a toxic algal bloom (red tide) that was record-breaking both in terms of density of dinoflagellates (9x higher than ever previously recorded) and duration (over a month from early April until mid-May compared to typical 1–2 week blooms see https://sccoos.org/california-hab-bulletin/red-tide ). This led not only to extraordinary fish kills (including within experimental and educational aquaria associated with Scripps that became contaminated by seawater intake) but also modified other physical (e.g. optical) and chemical properties of the near-shore marine environment, with a broad impact across that ecosystem. In many respects, it would have been surprising if fish spawning in 2020 was unaffected by this red tide. Moreover, the direction of the error in our prediction was consistent with these changes. The increased absorption of incident light due to the red, visibly opaque water occurred during our STT, and may have enhanced the STT magnitude, leading to an inflated temperature trigger and peak eggs prediction. The summer fish eggs, on the other hand, would be expected to be substantially reduced by the spring fish kills caused by the red tide.

Despite this potential source of error external to our model, any failure of an out-of-sample prediction deserves careful attention (indeed, the prediction from Ref. [ 15 ] still holds in 2020). The fact that the peak observed fish egg count in 2020 actually did not occur during the summer (with a substantially larger peak in the spring which again may be related to the red tide; Fig 4A ), prompted us to question whether the peak eggs should be defined as a summer event, or as an annual event. Relaxing this timing definition, and instead looking for an annual peak in eggs with a triggering event that occurs at any time before, completely disrupts the temperature trigger to peak eggs relationship, and not just for the 2020 data ( Fig 4B ; see Methods ). This suggests the possibility of overfitting in the base definitions, that is confirmed by allowing the date defining the boundary between spring and summer to vary ( Fig 4C ). We find that only small variations in the boundary date between spring and summer can substantially reduce the observed correlation ( Fig 4C ) which strongly suggests that the original observed relationship may have been a case of over-fitting.

A) 2020 fish eggs and temperature time series showing the timing of the red tide. The timing of the red tide was estimated based on the duration of anomalous dissolved oxygen levels measured by the Scripps Ocean Acidification Real-time (SOAR) Monitoring Program. B) Correlation between a triggering event that occurs any time before the annual peak eggs, and the annual peak eggs from 2013 to 2020 suggests possible overfitting (see Methods ). C) Sensitivity of the original 2013–2019 STT to peak summer eggs relationship to variation in the spring end date and summer start date further suggests overfitting.

Many ecological models and relationships are based on a large number of parameters, which renders the task of assessing model robustness in that high-dimensional parameter space generally difficult. However, even more difficult to assess is the robustness of assumptions that underlie the construction of the model itself, from which the model parameters arise. These assumptions may be non-quantitative (e.g. categorical), such as the choice of functional form or the inclusion or exclusion of model elements. Devising appropriate all-encompassing tests across this full space of possible hypotheses is neither feasible nor advisable, and so this source of uncertainty is often overlooked. Often, however, some assessment of the structural stability of models is both possible and highly enlightening (e.g. [ 31 ]) and should be encouraged. In the case of the STT to peak summer eggs relationship discussed here, the underlying definition of the spring and summer intervals was quantitative, and so was particularly easy to assess, but it is often the case with assumptions that they seem so obvious as to become invisible.

The process of making true out-of-sample predictions can account for both the difficulty of seeing potential sources of overfitting, and the difficulty of assessing them. Furthermore, publishing predictions before it is possible to assess their accuracy places in clear view any subsequent revisions of the model or relationship to account for new data. This may seem to go against natural intuition: if a model prediction can be tested, should it not be tested before publication, to improve confidence in the model? Certainly, a confidence threshold must be reached in order to justify publication (as it was here, where because of the nearly perfect fit any subsample with 3 or more points would reasonably produce successful out-of-sample predictions). However, we suggest that once that confidence threshold has been reached, leaving a true out-of-sample validation avenue open to future investigation is a valuable standard to aspire to, and we support the push towards quantitative prediction of truly out-of-sample natural data as a validation standard for ecological and earth systems science.

Materials and methods

Convergent cross mapping.

Convergent cross mapping was performed using the block_lnlp() function in rEDM v 0.7.3 [ 32 ]. The embedding was made with the raw egg abundance data and the daily-averaged SCCOOS temperature time series.

When performing CCM, the variable being predicted is the one being tested as a causal driver. As such, we used the egg abundance time series to predict temperature (thus measuring temperature’s effect on egg abundance). Although temperature data existed for every day, eggs were collected at inconsistent intervals, typically ranging from one collection every 2–5 days. Thus, in order to make a proper embedding, we filtered both temperature and egg abundance time series to only include temperature values for which a collection occurred on a given day, 6–8 days prior, and 13–15 days prior as well. This gave us a 3-dimensional embedding for fish eggs, with time lags of about 1 week, with accompanying temperature values.

Because both egg abundance and temperature are strongly seasonally driven, we needed to make sure we were not identifying shared information in the two variables driven by seasonality. To account for this, nearest neighbor selection only considered time points that were within 90 calendar days for our target prediction. Without doing this, increased library size will only increase the amount of seasonal information resolved in the embedding rather than actual causal inference.

Libraries of potential neighbors (points within 90 calendar days of the date of the target) were generated at random for each predicted point. Library sizes ranged from 10–80 points (increasing by increments of 5). Once the library was randomly generated, the nearest 4 neighbors (E+1, see (22)) in state space were selected and used to make a prediction. After a prediction was made on each temperature value, Pearson's correlation was calculated between observed and predicted values. This process was repeated 50 times for each library size.

STT calculation

As described in the text and illustrated in Fig 2 , STT was calculated by allowing the last day of the sliding window to move over the spring interval defined as April 1st to June 18th.

Sensitivity test

The annual peak in egg abundance was identified as the maximum egg abundance occurring between January 1 st and December 31 st within each year. The trigger-like value was found using the sliding window analysis described in the text and illustrated in Fig 2 , however the last day of the sliding window was allowed to move from January 28 th to the day immediately preceding the annual peak in eggs.

Sample collection

California Department of Fish and Wildlife permit (#4564) was used for the collection of plankton from the MPA’s. Vertical plankton tows (approximately weekly) were conducted off of the Scripps Pier (32.8328° N, -117.2713° W) from 2013 to 2019. A 1-meter diameter net with 505 micron mesh and a bottle attached to the cod end was lowered to the seafloor, approximately 5 meters, and out of the water 4 times, sampling a total of ~16 cubic meters of seawater. The net was then rinsed by lowering it into the water until the top of the net touched the surface and then raised back out. It is worth noting that this method only samples eggs suspended in the water column and does not effectively collect demersal eggs. There is some variation in the volume of water being sampled (e.g., water depth changes with tide), but since all collections went from seafloor to surface we do not expect any significant effect of egg depth profiles to have any significant effect on our measurement peak summer egg abundance. Currents could also affect sample volume but are rarely strong in the summer [ 33 ] and are therefore less likely to skew the value of peak eggs. The eggs captured at the pier all originated 0–3 days before the collection occurred since in Southern California water temperatures, most eggs for the fish species found there hatch within 72 hours [ 34 ] hence any eggs from a spawning event preceding the collection by up to 3 days could still be represented in our sample, depending on precise spawning location and currents. Using real-time current velocities, retrospective modeling found that most eggs collected at the Pier site likely originated within a few kilometers of the collection site [ 34 ]. The contents of the cod end were concentrated through a 330 micron mesh screen and then sorted under a microscope at 10X. The fish eggs were counted, placed in 1.5 mL tubes containing 95% ethanol, and stored at -20°C for at least 24 hours until further processing. At this step, the morphologically distinct eggs of the Northern anchovy ( Engraulis mordax ) and Pacific sardine ( Sardinops sagax ) are counted and stored separately because they do not require molecular methods for identification. The remaining eggs are identified through DNA barcoding. Comprehensive species lists are found in S2 and S3 Tables.

Supporting information

The strongest correlation we found was between STT and peak egg abundance (maximum correlation of 0.98). However, as found by [ 15 ], a strong, negative correlation exists between average winter temperatures and average summer egg abundance. Not surprisingly, there is also a strong correlation between peak egg abundance and average egg abundance for a given summer (A). Due to transitivity, there is also a strong correlation between average summer eggs and spring temperature triggers (B). Weaker correlations also exist between average winter temperatures and the finer scale temperature triggers (C) and peak summer egg abundance (D), however these are much weaker relationships (p> 0.1).

Shannon diversity (base e ) appears to be higher for peaks with lower abundance.

The proportional contribution that each of the identified species contributes to the annual peak summer egg abundance. The peak samples in each year are dominated by the eggs of a few species, with the dominant species varying from year-to-year.

A list of all 46 species identified in the samples from Scripps Pier from 2013–2019 and the number of eggs identified as each of those species within each year. The sampling effort by year is as follows: 2013 = 161, 2014 = 84, 2015 = 51, 2016 = 52, 2017 = 48, 2018 = 75, 2019 = 65.

A list of all 46 species identified in the samples from Scripps Pier from 2013–2019 and the proportion of samples they were present in within each year. The sampling effort by year is as follows: 2013 = 161, 2014 = 84, 2015 = 51, 2016 = 52, 2017 = 48, 2018 = 75, 2019 = 65.

Funding Statement

This work was supported by DoD-Strategic Environmental Research and Development Program 15 RC-2509 (GS), NSF DEB-1655203 (GS), NSF ABI-1667584 (GS), DOI USDI-NPS P20AC00527 (GS), the Scripps Institution of Oceanography Postdoctoral Fellowship (TL), the McQuown Fund and the McQuown Chair in Natural Sciences, University of California, San Diego (GS). Fish egg collection and identification was supported in part by the Richard Grand Foundation and the California Ocean Protection Council R/OPCSFAQ-12 (RB).

Data Availability

• PLoS One. 2020; 15(12): e0236541.

Decision Letter 0

PONE-D-20-21189

Temperature triggers provide quantitative predictions of multi-species fish spawning peaks

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Reviewer #1: This is an interesting analysis. It was a pleasure to read it. I favor publication. I am left with few questions, and a suggestion, which I hope the authors can address in a review.

1. What is the species composition of the egg catch? I am unsure with the statement that eggs are buoyant. Many are not, and it depends on the species.

2. Somewhat related to my previous question, Fig S2 is interesting and it shows decline of egg diversity in relation to peak eggs abundance. This is presumably driven by an increase of dominance of few or a single species. Can you elaborate on the species that dominate the samples, especially when egg abundance increases? At the end of the methods the authors indicate that the morphologically distinct anchovy and sardine eggs were removed, and the rest of the eggs were counted and identified to species using DNA barcoding. Would be great to see the species list.

5. This is a comment/suggestion. I suggest redirecting the focus of the research question toward a more mechanistic relationship between temperature and egg abundance. The author ask whether 'finer-timescale temperature dynamics provide information about finer-timescale fish egg abundance dynamics.' However, the striking relationships that they have uncovered between STT and peak egg abundance, in my view, is still an integrated measure rather than an examination of a finer scale relationship between temperature and eggs. There is still value in this relationship of course, but not of the same type suggested by the author. This analyses reveals potential mechanisms, pointing to the fact that large variations of water temperature during spring, may trigger massive spawning events during summer.

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Reviewer #1:  Yes:  Lorenzo Ciannelli

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Author response to Decision Letter 0

22 Oct 2020

Response to Reveiwer 1

Reviewer’s comment: What is the species composition of the egg catch? I am unsure with the statement that eggs are buoyant. Many are not, and it depends on the species.

The Reviewer raises an important point, and we’re sure many people will be interested in the composition of the samples. To address this, we have included two new supplemental tables. The first of these two tables, Table S2: Scripps Pier Species Abundance 2013–2019, lists all 46 of the species identified from the sampling at Scripps Pier from 2013 to 2019 and the number of eggs identified as each of these species within each year. The second of these two tables, Table S3: Scripps Pier Species Frequency 2013–2019, lists all 46 of the species and the proportion of samples (out of the yearly sampling effort) in which eggs from the species were observed within each year.

With regard to buoyancy, we have added a statement to the manuscript that our methods only effectively sample eggs suspended in the water column; we found very few eggs from species with demersal eggs – those were presumably stirred off the bottom by the net.

Reviewer’s comment: Somewhat related to my previous question, Fig S2 is interesting and it shows decline of egg diversity in relation to peak eggs abundance. This is presumably driven by an increase of dominance of few or a single species. Can you elaborate on the species that dominate the samples, especially when egg abundance increases? At the end of the methods the authors indicate that the morphologically distinct anchovy and sardine eggs were removed, and the rest of the eggs were counted and identified to species using DNA barcoding. Would be great to see the species list.

We thank the Reviewer for highlighting this opportunity for clarification. We have now included a new supplemental table, Table S1: Species composition of the peak summer egg abundance samples, that lists the proportion of the annual summer peak eggs that were identified as each species. This table highlights which species dominate the peak summer egg abundance samples. We have slightly expanded the main text in the discussion of synchrony to accommodate the addition of this table.

Reviewer’s comment: This is a comment/suggestion. I suggest redirecting the focus of the research question toward a more mechanistic relationship between temperature and egg abundance. The author ask whether 'finer-timescale temperature dynamics provide information about finer-timescale fish egg abundance dynamics.' However, the striking relationships that they have uncovered between STT and peak egg abundance, in my view, is still an integrated measure rather than an examination of a finer scale relationship between temperature and eggs. There is still value in this relationship of course, but not of the same type suggested by the author. This analyses reveals potential mechanisms, pointing to the fact that large variations of water temperature during spring, may trigger massive spawning events during summer.

While we greatly appreciate this suggestion, and would like to be able to pursue it, it is not currently possible to redirect our research question to address the finely resolved details of the mechanistic relationship between temperature and egg abundance. This is because we did not specifically measure a variable to demonstrate how temperature is acting to influence fish reproduction. Given that we are measuring a macroscopic output variable, egg abundance, it is difficult to determine whether the STT is having a direct physiological effect on the fish or whether it is related to other more proximate factors that drive increases in egg abundance. These are questions perhaps best answered by experimental manipulation; in the current experimental design of our study we could not discern the finer-scale details of the mechanism at play. The causality detection method (CCM), however, does verify (within these data) that there is a causal link here between temperature and egg abundance insofar as changes in temperature propagate to changes in egg abundance. Moreover, we show that the strength of the relationship identified here is dependent upon fine time scale measurements – both the STT and the peak summer egg abundance are captured through frequent measurements, daily in the case of STT and weekly in the case of peak summer egg abundance. In Figure 3A we showed that in smoothing the daily temperature datum, we lose the signal between the STT and peak summer egg abundance. Therefore, we focused our research question on fine time scale dynamics that are shown to be essential to this analysis, rather than a mechanistic relationship that we are unable to speak to, given the output variable we measured.

Submitted filename: Response to Reviewers_9.01.2020.pdf

Decision Letter 1

The importance of making testable predictions: a cautionary tale

PONE-D-20-21189R1

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Additional Editor Comments (optional):

The revised manuscript addresses All the comments/suggestions made by reviewer #1 including the availability/access to All the data that you have now nicely provided via the GitHub repository. The manuscript makes an interesting and transparent "tale" that I believe will be of great interest to the broad readership of PLOS ONE.

Acceptance letter

26 Nov 2020

Dear Dr. Sugihara:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

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15 Times Scientists Were Right And We Didn’t Listen

Posted: April 23, 2024 | Last updated: April 23, 2024

Have you ever watched a historical drama in which a scientist tries to convince a group of people who are set in their ways that germs are real or that disease isn’t spread via bad smells? You’re most likely sitting there, shaking your head, thinking how foolish these naysayers will look in the future.

The very nature of science is to observe, hypothesis, and experiment. New ideas can be hard to prove and are often met with resistance. So, it’s no surprise that there exists an extensive catalog of predictions by scientists that were initially disregarded. Here are just a few.

1. Climate Change Predictions

For decades, scientists have warned about the consequences of burning fossil fuels and climate change. Despite overwhelming evidence, many politicians and individuals have downplayed or denied the severity of the issue.

Since as early as 1896,  scientists  have been aware of climate change issues. Swedish scientist Svante Arrhenius predicted that fluctuations in atmospheric carbon dioxide levels could significantly impact surface temperatures through the greenhouse effect. Despite numerous scientists’ subsequent releases of substantial evidence, a segment of climate change deniers persists.

2. Cigarette Smoking and Cancer

Back in 1966, approximately 42% of the US adult population were cigarette smokers. Today, that number sits at 11.5%. By the 1950s,  scientists reported  that cigarettes were a cause of human disease, but this information was met with resistance by the tobacco companies and the general population.

In 2006, cigarette manufacturers were finally sued for their persistence in selling despite knowing the truth. Seventy years of public awareness and accountability are finally decreasing the number of smokers.

3. Germ Theory

Until the 19th century, it was widely believed that foul air, known as miasmas, was responsible for spreading diseases such as cholera and the bubonic plague. These miasmas were thought to be created from rotting matter.

While germ theory was known, it wasn’t until the late 19th century that it became an accepted truth. It wasn’t until Louis Pasteur’s work in 1861 that germ theory was accepted.

4. Dangers of Asbestos

Asbestos was found to harm human health starting in the early 20th century. However, widespread recognition of its dangers and regulatory actions took several decades. In 1934, researchers discovered a link between asbestos handling and disease.

The warnings about it didn’t come into effect until 1942. Despite this, the  asbestos industry  continued to thrive, with many denying or hiding the scientific evidence. Although most countries have  banned its use, the U.S. and Mexico continue to import it for some industries.

5. Overuse of Antibiotics

Scientists warned about the dangers of overprescribing antibiotics and the rise of antibiotic-resistant bacteria. Their warnings went unheeded by many, and widespread misuse of these drugs has led to the emergence of superbugs that are increasingly difficult to treat.

Many general practitioners prescribe antibiotics to people with colds and viruses, but these medicines have only a  placebo effect  on these conditions. Antibiotics are only effective in particular bacterial infections.

6. Extinction Crisis

Conservationists are often ignored and don’t receive sufficient attention or funding. Due to human activities such as deforestation, pollution, and habitat destruction, many species are being added to the endangered and extinction lists.

While some extinction is a natural phenomenon, scientists have shown that humans are accelerating its growth. A  report  from the WWF showed that animal species had reduced by 60% since the 1970s.

7. Theory of Evolution

Charles Darwin’s theory of evolution, published in 1859, contradicted what many religious and scientific circles believed at the time. Despite abundant evidence from fields such as paleontology, genetics, and comparative anatomy, acceptance of evolution took many years and continues to be contested by some.

Darwin’s theory revolutionized disciplines beyond biology.  Studies  have shown it helps us understand the fundamental principles of human life in other scientific fields, such as anthropology, psychology, and medicine. It also provides insight into the origin of diseases.

8. Diet and Health

Nutrition scientists have warned us against excessive consumption of processed foods, sugar, and unhealthy fats, linking them to obesity, diabetes, and heart disease. However, marketing campaigns promoting unhealthy products have often overshadowed these warnings.

With abundant conflicting information fed to us daily, knowing what is right and who to listen to can be difficult. It’s probably advisable to listen to science over the cereal jingle on the TV.

9. Lead Poisoning

Humankind has used lead for thousands of years. It’s been used to make coins, bullets, and armor. In the Middle Ages, lead was used in alchemy. In the Victorian era, it was widely used in cosmetics. In the 20th century, it was used in paint and gasoline.

Through science, we now know that lead poisoning can cause issues such as decreased mental capabilities, irritability, and, in acute cases, paralysis. The  Romans  knew there were lead issues, but that didn’t stop anyone from using it. It wasn’t until the 1970s that the EPA finally stopped its use in fuel and paint, and the public became more aware of the issue.

10. DNA and Heredity

The idea that genes encode hereditary traits faced initial skepticism. Yet, in the 1950s, scientists like James Watson and Francis Crick discovered the structure of DNA and its role in heredity, revolutionizing biology and genetics.

This discovery has helped immensely in the field of medicine. It can be used as a  preventative  measure and a diagnostic tool to determine someone’s susceptibility to particular conditions and diseases.

11. Plastics Pollution

The invention of plastics emerged in the early 20th century and gained popularity after World War II. By the 1960s and 70s, scientists started to become concerned about plastic pollution in the environment. Environmental scientists have warned about the pervasive problem of plastic pollution in oceans and ecosystems, which poses threats to marine life and human health.

Unfortunately, those in charge have taken a long time to raise awareness and make positive changes to reduce plastics. Some U.S. states take on the challenge of reducing plastics proactively, while others don’t have recycling programs.

12. Pesticide Use and Environmental Damage

Agricultural scientists have cautioned against the indiscriminate use of pesticides, which can harm beneficial insects, soil health, and water quality. However, intensive farming practices often prioritize short-term gains over long-term sustainability.

Take, for instance, the case of DDT, a potent pesticide once used extensively on crops. Despite its effectiveness in controlling pests, DDT’s persistence in the environment and its harmful impact on wildlife led to its eventual ban. Yet, the legacy of DDT continues to pose challenges as residues persist in ecosystems and still affect organisms today.

13. Overfishing and Marine Depletion

There’s a reason caviar is expensive—it’s hard to get because most sturgeon fish are on the endangered list. Marine biologists have warned about the depletion of fish stocks due to overfishing and destructive fishing methods.

The depletion of fish populations in the ocean disrupts the ecosystem’s delicate balance, leading to far-reaching consequences. Since many other marine species depend on fish as a food source, this depletion triggers a chain reaction throughout the ocean’s food web. Maybe it’s time to lay off the caviar.

14. Space Debris

Space is vast, so what’s a little space debris? Astronomers have raised concerns about the growing amount of space debris orbiting the Earth and estimate that about 34,000 pieces are floating around, which could cause a lot of damage if they were to hit something. Space junk is the result of humans shooting things into space.

Logistical and regulatory challenges have hampered efforts to retrieve space debris. Also, some methods — such as firing a missile to destroy a dead satellite — have been scrutinized, creating even more space junk.

15. Bloodletting

For thousands of years, many illnesses were treated by bloodletting. Several methods were used, one of which even included leeches. This may seem strange, given that having a good blood supply is essential for health. However, medical professionals believed it purged the body of sickness.

Studies  regarding bloodletting’s effectiveness were conducted in the 19th century. Of course, many were still skeptical about the results, and the method didn’t entirely lose favor until the end of the century.

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