write a hypothesis psychology

• Bipolar Disorder
• Therapy Center
• When To See a Therapist
• Types of Therapy
• Best Online Therapy
• Best Couples Therapy
• Best Family Therapy
• Managing Stress
• Sleep and Dreaming
• Understanding Emotions
• Self-Improvement
• Healthy Relationships
• Student Resources
• Personality Types
• Guided Meditations
• Verywell Mind Insights
• 2024 Verywell Mind 25
• Mental Health in the Classroom
• Editorial Process
• Meet Our Review Board
• Crisis Support

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."

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base

Methodology

• How to Write a Strong Hypothesis | Steps & Examples

How to Write a Strong Hypothesis | Steps & Examples

Published on May 6, 2022 by Shona McCombes . Revised on November 20, 2023.

A hypothesis is a statement that can be tested by scientific research. If you want to test a relationship between two or more variables, you need to write hypotheses before you start your experiment or data collection .

Example: Hypothesis

Daily apple consumption leads to fewer doctor’s visits.

Table of contents

What is a hypothesis, developing a hypothesis (with example), hypothesis examples, other interesting articles, frequently asked questions about writing hypotheses.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess – it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Variables in hypotheses

Hypotheses propose a relationship between two or more types of variables .

• An independent variable is something the researcher changes or controls.
• A dependent variable is something the researcher observes and measures.

If there are any control variables , extraneous variables , or confounding variables , be sure to jot those down as you go to minimize the chances that research bias  will affect your results.

In this example, the independent variable is exposure to the sun – the assumed cause . The dependent variable is the level of happiness – the assumed effect .

Receive feedback on language, structure, and formatting

Professional editors proofread and edit your paper by focusing on:

• Academic style
• Vague sentences
• Style consistency

See an example

Step 1. Ask a question

Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project.

Step 2. Do some preliminary research

Your initial answer to the question should be based on what is already known about the topic. Look for theories and previous studies to help you form educated assumptions about what your research will find.

At this stage, you might construct a conceptual framework to ensure that you’re embarking on a relevant topic . This can also help you identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalize more complex constructs.

Step 3. Formulate your hypothesis

Now you should have some idea of what you expect to find. Write your initial answer to the question in a clear, concise sentence.

4. Refine your hypothesis

You need to make sure your hypothesis is specific and testable. There are various ways of phrasing a hypothesis, but all the terms you use should have clear definitions, and the hypothesis should contain:

• The relevant variables
• The specific group being studied
• The predicted outcome of the experiment or analysis

5. Phrase your hypothesis in three ways

To identify the variables, you can write a simple prediction in  if…then form. The first part of the sentence states the independent variable and the second part states the dependent variable.

In academic research, hypotheses are more commonly phrased in terms of correlations or effects, where you directly state the predicted relationship between variables.

If you are comparing two groups, the hypothesis can state what difference you expect to find between them.

6. Write a null hypothesis

If your research involves statistical hypothesis testing , you will also have to write a null hypothesis . The null hypothesis is the default position that there is no association between the variables. The null hypothesis is written as H 0 , while the alternative hypothesis is H 1 or H a .

• H 0 : The number of lectures attended by first-year students has no effect on their final exam scores.
• H 1 : The number of lectures attended by first-year students has a positive effect on their final exam scores.

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

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

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

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

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

McCombes, S. (2023, November 20). How to Write a Strong Hypothesis | Steps & Examples. Scribbr. Retrieved June 25, 2024, from https://www.scribbr.com/methodology/hypothesis/

Is this article helpful?

Shona McCombes

Other students also liked, construct validity | definition, types, & examples, what is a conceptual framework | tips & examples, operationalization | a guide with examples, pros & cons, "i thought ai proofreading was useless but..".

I've been using Scribbr for years now and I know it's a service that won't disappoint. It does a good job spotting mistakes”

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, automatically generate references for free.

• Knowledge Base
• Methodology
• How to Write a Strong Hypothesis | Guide & Examples

How to Write a Strong Hypothesis | Guide & Examples

Published on 6 May 2022 by Shona McCombes .

A hypothesis is a statement that can be tested by scientific research. If you want to test a relationship between two or more variables, you need to write hypotheses before you start your experiment or data collection.

Table of contents

What is a hypothesis, developing a hypothesis (with example), hypothesis examples, frequently asked questions about writing hypotheses.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess – it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations, and statistical analysis of data).

Variables in hypotheses

Hypotheses propose a relationship between two or more variables . An independent variable is something the researcher changes or controls. A dependent variable is something the researcher observes and measures.

In this example, the independent variable is exposure to the sun – the assumed cause . The dependent variable is the level of happiness – the assumed effect .

Prevent plagiarism, run a free check.

Step 1: ask a question.

Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project.

Step 2: Do some preliminary research

Your initial answer to the question should be based on what is already known about the topic. Look for theories and previous studies to help you form educated assumptions about what your research will find.

At this stage, you might construct a conceptual framework to identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalise more complex constructs.

Step 3: Formulate your hypothesis

Now you should have some idea of what you expect to find. Write your initial answer to the question in a clear, concise sentence.

Step 4: Refine your hypothesis

You need to make sure your hypothesis is specific and testable. There are various ways of phrasing a hypothesis, but all the terms you use should have clear definitions, and the hypothesis should contain:

• The relevant variables
• The specific group being studied
• The predicted outcome of the experiment or analysis

Step 5: Phrase your hypothesis in three ways

To identify the variables, you can write a simple prediction in if … then form. The first part of the sentence states the independent variable and the second part states the dependent variable.

In academic research, hypotheses are more commonly phrased in terms of correlations or effects, where you directly state the predicted relationship between variables.

If you are comparing two groups, the hypothesis can state what difference you expect to find between them.

Step 6. Write a null hypothesis

If your research involves statistical hypothesis testing , you will also have to write a null hypothesis. The null hypothesis is the default position that there is no association between the variables. The null hypothesis is written as H 0 , while the alternative hypothesis is H 1 or H a .

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

A hypothesis is not just a guess. It should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations, and statistical analysis of data).

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

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

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

McCombes, S. (2022, May 06). How to Write a Strong Hypothesis | Guide & Examples. Scribbr. Retrieved 24 June 2024, from https://www.scribbr.co.uk/research-methods/hypothesis-writing/

Is this article helpful?

Shona McCombes

Other students also liked, operationalisation | a guide with examples, pros & cons, what is a conceptual framework | tips & examples, a quick guide to experimental design | 5 steps & examples.

2.4 Developing a Hypothesis

Learning objectives.

• Distinguish between a theory and a hypothesis.
• Discover how theories are used to generate hypotheses and how the results of studies can be used to further inform theories.
• Understand the characteristics of a good hypothesis.

Theories and Hypotheses

Before describing how to develop a hypothesis it is imporant to distinguish betwee a theory and a hypothesis. A  theory  is a coherent explanation or interpretation of one or more phenomena. Although theories can take a variety of forms, one thing they have in common is that they go beyond the phenomena they explain by including variables, structures, processes, functions, or organizing principles that have not been observed directly. Consider, for example, Zajonc’s theory of social facilitation and social inhibition. He proposed that being watched by others while performing a task creates a general state of physiological arousal, which increases the likelihood of the dominant (most likely) response. So for highly practiced tasks, being watched increases the tendency to make correct responses, but for relatively unpracticed tasks, being watched increases the tendency to make incorrect responses. Notice that this theory—which has come to be called drive theory—provides an explanation of both social facilitation and social inhibition that goes beyond the phenomena themselves by including concepts such as “arousal” and “dominant response,” along with processes such as the effect of arousal on the dominant response.

Outside of science, referring to an idea as a theory often implies that it is untested—perhaps no more than a wild guess. In science, however, the term theory has no such implication. A theory is simply an explanation or interpretation of a set of phenomena. It can be untested, but it can also be extensively tested, well supported, and accepted as an accurate description of the world by the scientific community. The theory of evolution by natural selection, for example, is a theory because it is an explanation of the diversity of life on earth—not because it is untested or unsupported by scientific research. On the contrary, the evidence for this theory is overwhelmingly positive and nearly all scientists accept its basic assumptions as accurate. Similarly, the “germ theory” of disease is a theory because it is an explanation of the origin of various diseases, not because there is any doubt that many diseases are caused by microorganisms that infect the body.

A  hypothesis , on the other hand, is a specific prediction about a new phenomenon that should be observed if a particular theory is accurate. It is an explanation that relies on just a few key concepts. Hypotheses are often specific predictions about what will happen in a particular study. They are developed by considering existing evidence and using reasoning to infer what will happen in the specific context of interest. Hypotheses are often but not always derived from theories. So a hypothesis is often a prediction based on a theory but some hypotheses are a-theoretical and only after a set of observations have been made, is a theory developed. This is because theories are broad in nature and they explain larger bodies of data. So if our research question is really original then we may need to collect some data and make some observation before we can develop a broader theory.

Theories and hypotheses always have this  if-then  relationship. “ If   drive theory is correct,  then  cockroaches should run through a straight runway faster, and a branching runway more slowly, when other cockroaches are present.” Although hypotheses are usually expressed as statements, they can always be rephrased as questions. “Do cockroaches run through a straight runway faster when other cockroaches are present?” Thus deriving hypotheses from theories is an excellent way of generating interesting research questions.

But how do researchers derive hypotheses from theories? One way is to generate a research question using the techniques discussed in this chapter  and then ask whether any theory implies an answer to that question. For example, you might wonder whether expressive writing about positive experiences improves health as much as expressive writing about traumatic experiences. Although this  question  is an interesting one  on its own, you might then ask whether the habituation theory—the idea that expressive writing causes people to habituate to negative thoughts and feelings—implies an answer. In this case, it seems clear that if the habituation theory is correct, then expressive writing about positive experiences should not be effective because it would not cause people to habituate to negative thoughts and feelings. A second way to derive hypotheses from theories is to focus on some component of the theory that has not yet been directly observed. For example, a researcher could focus on the process of habituation—perhaps hypothesizing that people should show fewer signs of emotional distress with each new writing session.

Among the very best hypotheses are those that distinguish between competing theories. For example, Norbert Schwarz and his colleagues considered two theories of how people make judgments about themselves, such as how assertive they are (Schwarz et al., 1991) [1] . Both theories held that such judgments are based on relevant examples that people bring to mind. However, one theory was that people base their judgments on the  number  of examples they bring to mind and the other was that people base their judgments on how  easily  they bring those examples to mind. To test these theories, the researchers asked people to recall either six times when they were assertive (which is easy for most people) or 12 times (which is difficult for most people). Then they asked them to judge their own assertiveness. Note that the number-of-examples theory implies that people who recalled 12 examples should judge themselves to be more assertive because they recalled more examples, but the ease-of-examples theory implies that participants who recalled six examples should judge themselves as more assertive because recalling the examples was easier. Thus the two theories made opposite predictions so that only one of the predictions could be confirmed. The surprising result was that participants who recalled fewer examples judged themselves to be more assertive—providing particularly convincing evidence in favor of the ease-of-retrieval theory over the number-of-examples theory.

Theory Testing

The primary way that scientific researchers use theories is sometimes called the hypothetico-deductive method  (although this term is much more likely to be used by philosophers of science than by scientists themselves). A researcher begins with a set of phenomena and either constructs a theory to explain or interpret them or chooses an existing theory to work with. He or she then makes a prediction about some new phenomenon that should be observed if the theory is correct. Again, this prediction is called a hypothesis. The researcher then conducts an empirical study to test the hypothesis. Finally, he or she reevaluates the theory in light of the new results and revises it if necessary. This process is usually conceptualized as a cycle because the researcher can then derive a new hypothesis from the revised theory, conduct a new empirical study to test the hypothesis, and so on. As  Figure 2.2  shows, this approach meshes nicely with the model of scientific research in psychology presented earlier in the textbook—creating a more detailed model of “theoretically motivated” or “theory-driven” research.

Figure 2.2 Hypothetico-Deductive Method Combined With the General Model of Scientific Research in Psychology Together they form a model of theoretically motivated research.

As an example, let us consider Zajonc’s research on social facilitation and inhibition. He started with a somewhat contradictory pattern of results from the research literature. He then constructed his drive theory, according to which being watched by others while performing a task causes physiological arousal, which increases an organism’s tendency to make the dominant response. This theory predicts social facilitation for well-learned tasks and social inhibition for poorly learned tasks. He now had a theory that organized previous results in a meaningful way—but he still needed to test it. He hypothesized that if his theory was correct, he should observe that the presence of others improves performance in a simple laboratory task but inhibits performance in a difficult version of the very same laboratory task. To test this hypothesis, one of the studies he conducted used cockroaches as subjects (Zajonc, Heingartner, & Herman, 1969) [2] . The cockroaches ran either down a straight runway (an easy task for a cockroach) or through a cross-shaped maze (a difficult task for a cockroach) to escape into a dark chamber when a light was shined on them. They did this either while alone or in the presence of other cockroaches in clear plastic “audience boxes.” Zajonc found that cockroaches in the straight runway reached their goal more quickly in the presence of other cockroaches, but cockroaches in the cross-shaped maze reached their goal more slowly when they were in the presence of other cockroaches. Thus he confirmed his hypothesis and provided support for his drive theory. (Zajonc also showed that drive theory existed in humans (Zajonc & Sales, 1966) [3] in many other studies afterward).

Incorporating Theory into Your Research

When you write your research report or plan your presentation, be aware that there are two basic ways that researchers usually include theory. The first is to raise a research question, answer that question by conducting a new study, and then offer one or more theories (usually more) to explain or interpret the results. This format works well for applied research questions and for research questions that existing theories do not address. The second way is to describe one or more existing theories, derive a hypothesis from one of those theories, test the hypothesis in a new study, and finally reevaluate the theory. This format works well when there is an existing theory that addresses the research question—especially if the resulting hypothesis is surprising or conflicts with a hypothesis derived from a different theory.

To use theories in your research will not only give you guidance in coming up with experiment ideas and possible projects, but it lends legitimacy to your work. Psychologists have been interested in a variety of human behaviors and have developed many theories along the way. Using established theories will help you break new ground as a researcher, not limit you from developing your own ideas.

Characteristics of a Good Hypothesis

There are three general characteristics of a good hypothesis. First, a good hypothesis must be testable and falsifiable . We must be able to test the hypothesis using the methods of science and if you’ll recall Popper’s falsifiability criterion, it must be possible to gather evidence that will disconfirm the hypothesis if it is indeed false. Second, a good hypothesis must be  logical. As described above, hypotheses are more than just a random guess. Hypotheses should be informed by previous theories or observations and logical reasoning. Typically, we begin with a broad and general theory and use  deductive reasoning to generate a more specific hypothesis to test based on that theory. Occasionally, however, when there is no theory to inform our hypothesis, we use  inductive reasoning  which involves using specific observations or research findings to form a more general hypothesis. Finally, the hypothesis should be  positive.  That is, the hypothesis should make a positive statement about the existence of a relationship or effect, rather than a statement that a relationship or effect does not exist. As scientists, we don’t set out to show that relationships do not exist or that effects do not occur so our hypotheses should not be worded in a way to suggest that an effect or relationship does not exist. The nature of science is to assume that something does not exist and then seek to find evidence to prove this wrong, to show that really it does exist. That may seem backward to you but that is the nature of the scientific method. The underlying reason for this is beyond the scope of this chapter but it has to do with statistical theory.

Key Takeaways

• A theory is broad in nature and explains larger bodies of data. A hypothesis is more specific and makes a prediction about the outcome of a particular study.
• Working with theories is not “icing on the cake.” It is a basic ingredient of psychological research.
• Like other scientists, psychologists use the hypothetico-deductive method. They construct theories to explain or interpret phenomena (or work with existing theories), derive hypotheses from their theories, test the hypotheses, and then reevaluate the theories in light of the new results.
• Practice: Find a recent empirical research report in a professional journal. Read the introduction and highlight in different colors descriptions of theories and hypotheses.
• Schwarz, N., Bless, H., Strack, F., Klumpp, G., Rittenauer-Schatka, H., & Simons, A. (1991). Ease of retrieval as information: Another look at the availability heuristic.  Journal of Personality and Social Psychology, 61 , 195–202. ↵
• Zajonc, R. B., Heingartner, A., & Herman, E. M. (1969). Social enhancement and impairment of performance in the cockroach.  Journal of Personality and Social Psychology, 13 , 83–92. ↵
• Zajonc, R.B. & Sales, S.M. (1966). Social facilitation of dominant and subordinate responses. Journal of Experimental Social Psychology, 2 , 160-168. ↵

Share This Book

• Increase Font Size

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

Developing a Hypothesis

Rajiv S. Jhangiani; I-Chant A. Chiang; Carrie Cuttler; and Dana C. Leighton

Learning Objectives

• Distinguish between a theory and a hypothesis.
• Discover how theories are used to generate hypotheses and how the results of studies can be used to further inform theories.
• Understand the characteristics of a good hypothesis.

Theories and Hypotheses

Before describing how to develop a hypothesis, it is important to distinguish between a theory and a hypothesis. A  theory  is a coherent explanation or interpretation of one or more phenomena. Although theories can take a variety of forms, one thing they have in common is that they go beyond the phenomena they explain by including variables, structures, processes, functions, or organizing principles that have not been observed directly. Consider, for example, Zajonc’s theory of social facilitation and social inhibition (1965) [1] . He proposed that being watched by others while performing a task creates a general state of physiological arousal, which increases the likelihood of the dominant (most likely) response. So for highly practiced tasks, being watched increases the tendency to make correct responses, but for relatively unpracticed tasks, being watched increases the tendency to make incorrect responses. Notice that this theory—which has come to be called drive theory—provides an explanation of both social facilitation and social inhibition that goes beyond the phenomena themselves by including concepts such as “arousal” and “dominant response,” along with processes such as the effect of arousal on the dominant response.

Outside of science, referring to an idea as a theory often implies that it is untested—perhaps no more than a wild guess. In science, however, the term theory has no such implication. A theory is simply an explanation or interpretation of a set of phenomena. It can be untested, but it can also be extensively tested, well supported, and accepted as an accurate description of the world by the scientific community. The theory of evolution by natural selection, for example, is a theory because it is an explanation of the diversity of life on earth—not because it is untested or unsupported by scientific research. On the contrary, the evidence for this theory is overwhelmingly positive and nearly all scientists accept its basic assumptions as accurate. Similarly, the “germ theory” of disease is a theory because it is an explanation of the origin of various diseases, not because there is any doubt that many diseases are caused by microorganisms that infect the body.

A  hypothesis , on the other hand, is a specific prediction about a new phenomenon that should be observed if a particular theory is accurate. It is an explanation that relies on just a few key concepts. Hypotheses are often specific predictions about what will happen in a particular study. They are developed by considering existing evidence and using reasoning to infer what will happen in the specific context of interest. Hypotheses are often but not always derived from theories. So a hypothesis is often a prediction based on a theory but some hypotheses are a-theoretical and only after a set of observations have been made, is a theory developed. This is because theories are broad in nature and they explain larger bodies of data. So if our research question is really original then we may need to collect some data and make some observations before we can develop a broader theory.

Theories and hypotheses always have this  if-then  relationship. “ If   drive theory is correct,  then  cockroaches should run through a straight runway faster, and a branching runway more slowly, when other cockroaches are present.” Although hypotheses are usually expressed as statements, they can always be rephrased as questions. “Do cockroaches run through a straight runway faster when other cockroaches are present?” Thus deriving hypotheses from theories is an excellent way of generating interesting research questions.

But how do researchers derive hypotheses from theories? One way is to generate a research question using the techniques discussed in this chapter  and then ask whether any theory implies an answer to that question. For example, you might wonder whether expressive writing about positive experiences improves health as much as expressive writing about traumatic experiences. Although this  question  is an interesting one  on its own, you might then ask whether the habituation theory—the idea that expressive writing causes people to habituate to negative thoughts and feelings—implies an answer. In this case, it seems clear that if the habituation theory is correct, then expressive writing about positive experiences should not be effective because it would not cause people to habituate to negative thoughts and feelings. A second way to derive hypotheses from theories is to focus on some component of the theory that has not yet been directly observed. For example, a researcher could focus on the process of habituation—perhaps hypothesizing that people should show fewer signs of emotional distress with each new writing session.

Among the very best hypotheses are those that distinguish between competing theories. For example, Norbert Schwarz and his colleagues considered two theories of how people make judgments about themselves, such as how assertive they are (Schwarz et al., 1991) [2] . Both theories held that such judgments are based on relevant examples that people bring to mind. However, one theory was that people base their judgments on the  number  of examples they bring to mind and the other was that people base their judgments on how  easily  they bring those examples to mind. To test these theories, the researchers asked people to recall either six times when they were assertive (which is easy for most people) or 12 times (which is difficult for most people). Then they asked them to judge their own assertiveness. Note that the number-of-examples theory implies that people who recalled 12 examples should judge themselves to be more assertive because they recalled more examples, but the ease-of-examples theory implies that participants who recalled six examples should judge themselves as more assertive because recalling the examples was easier. Thus the two theories made opposite predictions so that only one of the predictions could be confirmed. The surprising result was that participants who recalled fewer examples judged themselves to be more assertive—providing particularly convincing evidence in favor of the ease-of-retrieval theory over the number-of-examples theory.

Theory Testing

The primary way that scientific researchers use theories is sometimes called the hypothetico-deductive method  (although this term is much more likely to be used by philosophers of science than by scientists themselves). Researchers begin with a set of phenomena and either construct a theory to explain or interpret them or choose an existing theory to work with. They then make a prediction about some new phenomenon that should be observed if the theory is correct. Again, this prediction is called a hypothesis. The researchers then conduct an empirical study to test the hypothesis. Finally, they reevaluate the theory in light of the new results and revise it if necessary. This process is usually conceptualized as a cycle because the researchers can then derive a new hypothesis from the revised theory, conduct a new empirical study to test the hypothesis, and so on. As  Figure 2.3  shows, this approach meshes nicely with the model of scientific research in psychology presented earlier in the textbook—creating a more detailed model of “theoretically motivated” or “theory-driven” research.

As an example, let us consider Zajonc’s research on social facilitation and inhibition. He started with a somewhat contradictory pattern of results from the research literature. He then constructed his drive theory, according to which being watched by others while performing a task causes physiological arousal, which increases an organism’s tendency to make the dominant response. This theory predicts social facilitation for well-learned tasks and social inhibition for poorly learned tasks. He now had a theory that organized previous results in a meaningful way—but he still needed to test it. He hypothesized that if his theory was correct, he should observe that the presence of others improves performance in a simple laboratory task but inhibits performance in a difficult version of the very same laboratory task. To test this hypothesis, one of the studies he conducted used cockroaches as subjects (Zajonc, Heingartner, & Herman, 1969) [3] . The cockroaches ran either down a straight runway (an easy task for a cockroach) or through a cross-shaped maze (a difficult task for a cockroach) to escape into a dark chamber when a light was shined on them. They did this either while alone or in the presence of other cockroaches in clear plastic “audience boxes.” Zajonc found that cockroaches in the straight runway reached their goal more quickly in the presence of other cockroaches, but cockroaches in the cross-shaped maze reached their goal more slowly when they were in the presence of other cockroaches. Thus he confirmed his hypothesis and provided support for his drive theory. (Zajonc also showed that drive theory existed in humans [Zajonc & Sales, 1966] [4] in many other studies afterward).

Incorporating Theory into Your Research

When you write your research report or plan your presentation, be aware that there are two basic ways that researchers usually include theory. The first is to raise a research question, answer that question by conducting a new study, and then offer one or more theories (usually more) to explain or interpret the results. This format works well for applied research questions and for research questions that existing theories do not address. The second way is to describe one or more existing theories, derive a hypothesis from one of those theories, test the hypothesis in a new study, and finally reevaluate the theory. This format works well when there is an existing theory that addresses the research question—especially if the resulting hypothesis is surprising or conflicts with a hypothesis derived from a different theory.

To use theories in your research will not only give you guidance in coming up with experiment ideas and possible projects, but it lends legitimacy to your work. Psychologists have been interested in a variety of human behaviors and have developed many theories along the way. Using established theories will help you break new ground as a researcher, not limit you from developing your own ideas.

Characteristics of a Good Hypothesis

There are three general characteristics of a good hypothesis. First, a good hypothesis must be testable and falsifiable . We must be able to test the hypothesis using the methods of science and if you’ll recall Popper’s falsifiability criterion, it must be possible to gather evidence that will disconfirm the hypothesis if it is indeed false. Second, a good hypothesis must be logical. As described above, hypotheses are more than just a random guess. Hypotheses should be informed by previous theories or observations and logical reasoning. Typically, we begin with a broad and general theory and use  deductive reasoning to generate a more specific hypothesis to test based on that theory. Occasionally, however, when there is no theory to inform our hypothesis, we use  inductive reasoning  which involves using specific observations or research findings to form a more general hypothesis. Finally, the hypothesis should be positive. That is, the hypothesis should make a positive statement about the existence of a relationship or effect, rather than a statement that a relationship or effect does not exist. As scientists, we don’t set out to show that relationships do not exist or that effects do not occur so our hypotheses should not be worded in a way to suggest that an effect or relationship does not exist. The nature of science is to assume that something does not exist and then seek to find evidence to prove this wrong, to show that it really does exist. That may seem backward to you but that is the nature of the scientific method. The underlying reason for this is beyond the scope of this chapter but it has to do with statistical theory.

• Zajonc, R. B. (1965). Social facilitation.  Science, 149 , 269–274 ↵
• Schwarz, N., Bless, H., Strack, F., Klumpp, G., Rittenauer-Schatka, H., & Simons, A. (1991). Ease of retrieval as information: Another look at the availability heuristic.  Journal of Personality and Social Psychology, 61 , 195–202. ↵
• Zajonc, R. B., Heingartner, A., & Herman, E. M. (1969). Social enhancement and impairment of performance in the cockroach.  Journal of Personality and Social Psychology, 13 , 83–92. ↵
• Zajonc, R.B. & Sales, S.M. (1966). Social facilitation of dominant and subordinate responses. Journal of Experimental Social Psychology, 2 , 160-168. ↵

A coherent explanation or interpretation of one or more phenomena.

A specific prediction about a new phenomenon that should be observed if a particular theory is accurate.

A cyclical process of theory development, starting with an observed phenomenon, then developing or using a theory to make a specific prediction of what should happen if that theory is correct, testing that prediction, refining the theory in light of the findings, and using that refined theory to develop new hypotheses, and so on.

The ability to test the hypothesis using the methods of science and the possibility to gather evidence that will disconfirm the hypothesis if it is indeed false.

Developing a Hypothesis Copyright © by Rajiv S. Jhangiani; I-Chant A. Chiang; Carrie Cuttler; and Dana C. Leighton is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

Share This Book

Educational resources and simple solutions for your research journey

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

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

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

Table of Contents

What is a hypothesis ?  

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

What is a research hypothesis ?  

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

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

Characteristics of a good hypothesis  

Here are the characteristics of a good hypothesis :  

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

How to create an effective research hypothesis  

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

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

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

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

How to write a research hypothesis  

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

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

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

Population: athletes  

Independent variable: daily cold water showers  

Dependent variable: endurance  

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

Research hypothesis checklist  

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

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

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

Types of research hypothesis  

Different types of research hypothesis are used in scientific research:  

1. Null hypothesis:

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

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

2. Alternative hypothesis:

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

Example: “ The newly identified virus is zoonotic .”  

3. Directional hypothesis :

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

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

4. Non-directional hypothesis:

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

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

5. Simple hypothesis :

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

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

6 . Complex hypothesis :

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

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

7. Associative hypothesis:  

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

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

8 . Causal hypothesis:

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

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

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

Research hypothesis examples  

Here are some good research hypothesis examples :  

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

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

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

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

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

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

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

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

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

Importance of testable hypothesis  

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

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

Frequently Asked Questions (FAQs) on research hypothesis  

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

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

2. When to reject null hypothesis ?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

9. Can research hypotheses be used in qualitative research?

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

Researcher.Life is a subscription-based platform that unifies the best AI tools and services designed to speed up, simplify, and streamline every step of a researcher’s journey. The Researcher.Life All Access Pack is a one-of-a-kind subscription that unlocks full access to an AI writing assistant, literature recommender, journal finder, scientific illustration tool, and exclusive discounts on professional publication services from Editage.  

Based on 21+ years of experience in academia, Researcher.Life All Access empowers researchers to put their best research forward and move closer to success. Explore our top AI Tools pack, AI Tools + Publication Services pack, or Build Your Own Plan. Find everything a researcher needs to succeed, all in one place –  Get All Access now starting at just $17 a month !    

Related Posts

Thesis Defense: How to Ace this Crucial Step

What is Independent Publishing in Academia?

Hypothesis ( AQA A Level Psychology )

Revision note.

Psychology Content Creator

• A hypothesis is a testable statement written as a prediction of what the researcher expects to find as a result of their experiment
• A hypothesis should be no more than one sentence long
• The hypothesis needs to include the independent variable (IV) and the dependent variable (DV)
• For example - stating that you will measure ‘aggression’ is not enough ('aggression' has not been operationalised)
• by exposing some children to an aggressive adult model whilst other children are not exposed to an aggressive adult model (operationalisation of the IV) 
• number of imitative and non-imitative acts of aggression performed by the child (operationalisation of the DV)

The Experimental Hypothesis

• Children who are exposed to an aggressive adult model will perform more acts of imitative and non-imitative aggression than children who have not been exposed to an aggressive adult model
• The experimental hypothesis can be written as a  directional hypothesis or as a non-directional hypothesis

The Experimental Hypothesis: Directional 

• A directional experimental hypothesis (also known as one-tailed)  predicts the direction of the change/difference (it anticipates more specifically what might happen)
• A directional hypothesis is usually used when there is previous research which support a particular theory or outcome i.e. what a researcher might expect to happen
• Participants who drink 200ml of an energy drink 5 minutes before running 100m will be faster (in seconds) than participants who drink 200ml of water 5 minutes before running 100m
• Participants who learn a poem in a room in which loud music is playing will recall less of the poem's content than participants who learn the same poem in a silent room

 The Experimental Hypothesis: Non-Directional 

• A non-directional experimental hypothesis (also known as two -tailed) does not predict the direction of the change/difference (it is an 'open goal' i.e. anything could happen)
• A non-directional hypothesis is usually used when there is either no or little previous research which support a particular theory or outcome i.e. what the researcher cannot be confident as to what will happen
• There will be a difference in time taken (in seconds) to run 100m depending on whether participants have drunk 200ml of an energy drink or 200ml of water 5 minutes before running 
• There will be a difference in recall of a poem depending on whether participants learn the poem in a room in which loud music is playing or in a silent room

The Null Hypothesis

• All published psychology research must include the null hypothesis
• There will be no difference in children's acts of imitative and non-imitative aggression depending on whether they have observed an aggressive adult model or a non-aggressive adult model
• The null hypothesis has to begin with the idea that the IV will have no effect on the DV  because until the experiment is run and the results are analysed it is impossible to state anything else! 
• To put this in 'laymen's terms: if you bought a lottery ticket you could not predict that you are going to win the jackpot: you have to wait for the results to find out (spoiler alert: the chances of this happening are soooo low that you might as well save your cash!)
• There will be no difference in time taken (in seconds) to run 100m depending on whether participants have drunk 200ml of an energy drink or 200ml of water 5 minutes before running 
• There will be no difference in recall of a poem depending on whether participants learn the poem in a room in which loud music is playing or in a silent room
• (NB this is not quite so slick and easy with a directional hypothesis as this sort of hypothesis will never begin with 'There will be a difference')
• this is why the null hypothesis is so important - it tells the researcher whether or not their experiment has shown a difference in conditions (which is generally what they want to see, otherwise it's back to the drawing board...)

Worked example

Jim wants to test the theory that chocolate helps your ability to solve word-search puzzles

He believes that sugar helps memory as he has read some research on this in a text book

He puts up a poster in his sixth-form common room asking for people to take part after school one day and explains that they will be required to play two memory games, where eating chocolate will be involved

(a)  Should Jim use a directional hypothesis in this study? Explain your answer (2 marks)

(b)  Write a suitable hypothesis for this study. (4 marks)

a) Jim should use a directional hypothesis (1 mark)

    because previous research exists that states what might happen (2 nd mark)

b)  'Participants will remember more items from a shopping list in a memory game within the hour after eating 50g of chocolate, compared to when they have not consumed any chocolate'

• 1 st mark for directional
• 2 nd mark for IV- eating chocolate
• 3 rd mark for DV- number of items remembered
• 4 th mark for operationalising both IV & DV
• If you write a non-directional or null hypothesis the mark is 0
• If you do not get the direction correct the mark is zero
• Remember to operationalise the IV & DV

You've read 0 of your 0 free revision notes

Get unlimited access.

to absolutely everything:

• Downloadable PDFs
• Unlimited Revision Notes
• Topic Questions
• Past Papers
• Model Answers
• Videos (Maths and Science)

Join the 100,000 + Students that ❤️ Save My Exams

the (exam) results speak for themselves:

Did this page help you?

Author: Claire Neeson

Claire has been teaching for 34 years, in the UK and overseas. She has taught GCSE, A-level and IB Psychology which has been a lot of fun and extremely exhausting! Claire is now a freelance Psychology teacher and content creator, producing textbooks, revision notes and (hopefully) exciting and interactive teaching materials for use in the classroom and for exam prep. Her passion (apart from Psychology of course) is roller skating and when she is not working (or watching 'Coronation Street') she can be found busting some impressive moves on her local roller rink.

• Resources Home 🏠
• Try SciSpace Copilot
• Search research papers
• Add Copilot Extension
• Try AI Detector
• Try Paraphraser
• Try Citation Generator
• April Papers
• June Papers
• July Papers

The Craft of Writing a Strong Hypothesis

Table of Contents

Writing a hypothesis is one of the essential elements of a scientific research paper. It needs to be to the point, clearly communicating what your research is trying to accomplish. A blurry, drawn-out, or complexly-structured hypothesis can confuse your readers. Or worse, the editor and peer reviewers.

A captivating hypothesis is not too intricate. This blog will take you through the process so that, by the end of it, you have a better idea of how to convey your research paper's intent in just one sentence.

What is a Hypothesis?

The first step in your scientific endeavor, a hypothesis, is a strong, concise statement that forms the basis of your research. It is not the same as a thesis statement , which is a brief summary of your research paper .

The sole purpose of a hypothesis is to predict your paper's findings, data, and conclusion. It comes from a place of curiosity and intuition . When you write a hypothesis, you're essentially making an educated guess based on scientific prejudices and evidence, which is further proven or disproven through the scientific method.

The reason for undertaking research is to observe a specific phenomenon. A hypothesis, therefore, lays out what the said phenomenon is. And it does so through two variables, an independent and dependent variable.

The independent variable is the cause behind the observation, while the dependent variable is the effect of the cause. A good example of this is “mixing red and blue forms purple.” In this hypothesis, mixing red and blue is the independent variable as you're combining the two colors at your own will. The formation of purple is the dependent variable as, in this case, it is conditional to the independent variable.

Different Types of Hypotheses‌

Types of hypotheses

Some would stand by the notion that there are only two types of hypotheses: a Null hypothesis and an Alternative hypothesis. While that may have some truth to it, it would be better to fully distinguish the most common forms as these terms come up so often, which might leave you out of context.

Apart from Null and Alternative, there are Complex, Simple, Directional, Non-Directional, Statistical, and Associative and casual hypotheses. They don't necessarily have to be exclusive, as one hypothesis can tick many boxes, but knowing the distinctions between them will make it easier for you to construct your own.

1. Null hypothesis

A null hypothesis proposes no relationship between two variables. Denoted by H 0 , it is a negative statement like “Attending physiotherapy sessions does not affect athletes' on-field performance.” Here, the author claims physiotherapy sessions have no effect on on-field performances. Even if there is, it's only a coincidence.

2. Alternative hypothesis

Considered to be the opposite of a null hypothesis, an alternative hypothesis is donated as H1 or Ha. It explicitly states that the dependent variable affects the independent variable. A good  alternative hypothesis example is “Attending physiotherapy sessions improves athletes' on-field performance.” or “Water evaporates at 100 °C. ” The alternative hypothesis further branches into directional and non-directional.

• Directional hypothesis: A hypothesis that states the result would be either positive or negative is called directional hypothesis. It accompanies H1 with either the ‘<' or ‘>' sign.
• Non-directional hypothesis: A non-directional hypothesis only claims an effect on the dependent variable. It does not clarify whether the result would be positive or negative. The sign for a non-directional hypothesis is ‘≠.'

3. Simple hypothesis

A simple hypothesis is a statement made to reflect the relation between exactly two variables. One independent and one dependent. Consider the example, “Smoking is a prominent cause of lung cancer." The dependent variable, lung cancer, is dependent on the independent variable, smoking.

4. Complex hypothesis

In contrast to a simple hypothesis, a complex hypothesis implies the relationship between multiple independent and dependent variables. For instance, “Individuals who eat more fruits tend to have higher immunity, lesser cholesterol, and high metabolism.” The independent variable is eating more fruits, while the dependent variables are higher immunity, lesser cholesterol, and high metabolism.

5. Associative and casual hypothesis

Associative and casual hypotheses don't exhibit how many variables there will be. They define the relationship between the variables. In an associative hypothesis, changing any one variable, dependent or independent, affects others. In a casual hypothesis, the independent variable directly affects the dependent.

6. Empirical hypothesis

Also referred to as the working hypothesis, an empirical hypothesis claims a theory's validation via experiments and observation. This way, the statement appears justifiable and different from a wild guess.

Say, the hypothesis is “Women who take iron tablets face a lesser risk of anemia than those who take vitamin B12.” This is an example of an empirical hypothesis where the researcher  the statement after assessing a group of women who take iron tablets and charting the findings.

7. Statistical hypothesis

The point of a statistical hypothesis is to test an already existing hypothesis by studying a population sample. Hypothesis like “44% of the Indian population belong in the age group of 22-27.” leverage evidence to prove or disprove a particular statement.

Characteristics of a Good Hypothesis

Writing a hypothesis is essential as it can make or break your research for you. That includes your chances of getting published in a journal. So when you're designing one, keep an eye out for these pointers:

• A research hypothesis has to be simple yet clear to look justifiable enough.
• It has to be testable — your research would be rendered pointless if too far-fetched into reality or limited by technology.
• It has to be precise about the results —what you are trying to do and achieve through it should come out in your hypothesis.
• A research hypothesis should be self-explanatory, leaving no doubt in the reader's mind.
• If you are developing a relational hypothesis, you need to include the variables and establish an appropriate relationship among them.
• A hypothesis must keep and reflect the scope for further investigations and experiments.

Separating a Hypothesis from a Prediction

Outside of academia, hypothesis and prediction are often used interchangeably. In research writing, this is not only confusing but also incorrect. And although a hypothesis and prediction are guesses at their core, there are many differences between them.

A hypothesis is an educated guess or even a testable prediction validated through research. It aims to analyze the gathered evidence and facts to define a relationship between variables and put forth a logical explanation behind the nature of events.

Predictions are assumptions or expected outcomes made without any backing evidence. They are more fictionally inclined regardless of where they originate from.

For this reason, a hypothesis holds much more weight than a prediction. It sticks to the scientific method rather than pure guesswork. "Planets revolve around the Sun." is an example of a hypothesis as it is previous knowledge and observed trends. Additionally, we can test it through the scientific method.

Whereas "COVID-19 will be eradicated by 2030." is a prediction. Even though it results from past trends, we can't prove or disprove it. So, the only way this gets validated is to wait and watch if COVID-19 cases end by 2030.

Finally, How to Write a Hypothesis

Quick tips on writing a hypothesis

1.  Be clear about your research question

A hypothesis should instantly address the research question or the problem statement. To do so, you need to ask a question. Understand the constraints of your undertaken research topic and then formulate a simple and topic-centric problem. Only after that can you develop a hypothesis and further test for evidence.

2. Carry out a recce

Once you have your research's foundation laid out, it would be best to conduct preliminary research. Go through previous theories, academic papers, data, and experiments before you start curating your research hypothesis. It will give you an idea of your hypothesis's viability or originality.

Making use of references from relevant research papers helps draft a good research hypothesis. SciSpace Discover offers a repository of over 270 million research papers to browse through and gain a deeper understanding of related studies on a particular topic. Additionally, you can use SciSpace Copilot , your AI research assistant, for reading any lengthy research paper and getting a more summarized context of it. A hypothesis can be formed after evaluating many such summarized research papers. Copilot also offers explanations for theories and equations, explains paper in simplified version, allows you to highlight any text in the paper or clip math equations and tables and provides a deeper, clear understanding of what is being said. This can improve the hypothesis by helping you identify potential research gaps.

3. Create a 3-dimensional hypothesis

Variables are an essential part of any reasonable hypothesis. So, identify your independent and dependent variable(s) and form a correlation between them. The ideal way to do this is to write the hypothetical assumption in the ‘if-then' form. If you use this form, make sure that you state the predefined relationship between the variables.

In another way, you can choose to present your hypothesis as a comparison between two variables. Here, you must specify the difference you expect to observe in the results.

4. Write the first draft

Now that everything is in place, it's time to write your hypothesis. For starters, create the first draft. In this version, write what you expect to find from your research.

Clearly separate your independent and dependent variables and the link between them. Don't fixate on syntax at this stage. The goal is to ensure your hypothesis addresses the issue.

5. Proof your hypothesis

After preparing the first draft of your hypothesis, you need to inspect it thoroughly. It should tick all the boxes, like being concise, straightforward, relevant, and accurate. Your final hypothesis has to be well-structured as well.

Research projects are an exciting and crucial part of being a scholar. And once you have your research question, you need a great hypothesis to begin conducting research. Thus, knowing how to write a hypothesis is very important.

Now that you have a firmer grasp on what a good hypothesis constitutes, the different kinds there are, and what process to follow, you will find it much easier to write your hypothesis, which ultimately helps your research.

Now it's easier than ever to streamline your research workflow with SciSpace Discover . Its integrated, comprehensive end-to-end platform for research allows scholars to easily discover, write and publish their research and fosters collaboration.

It includes everything you need, including a repository of over 270 million research papers across disciplines, SEO-optimized summaries and public profiles to show your expertise and experience.

If you found these tips on writing a research hypothesis useful, head over to our blog on Statistical Hypothesis Testing to learn about the top researchers, papers, and institutions in this domain.

Frequently Asked Questions (FAQs)

1. what is the definition of hypothesis.

According to the Oxford dictionary, a hypothesis is defined as “An idea or explanation of something that is based on a few known facts, but that has not yet been proved to be true or correct”.

2. What is an example of hypothesis?

The hypothesis is a statement that proposes a relationship between two or more variables. An example: "If we increase the number of new users who join our platform by 25%, then we will see an increase in revenue."

3. What is an example of null hypothesis?

A null hypothesis is a statement that there is no relationship between two variables. The null hypothesis is written as H0. The null hypothesis states that there is no effect. For example, if you're studying whether or not a particular type of exercise increases strength, your null hypothesis will be "there is no difference in strength between people who exercise and people who don't."

4. What are the types of research?

• Fundamental research

• Applied research

• Qualitative research

• Quantitative research

• Mixed research

• Exploratory research

• Longitudinal research

• Cross-sectional research

• Field research

• Laboratory research

• Fixed research

• Flexible research

• Action research

• Policy research

• Classification research

• Comparative research

• Causal research

• Inductive research

• Deductive research

5. How to write a hypothesis?

• Your hypothesis should be able to predict the relationship and outcome.

• Avoid wordiness by keeping it simple and brief.

• Your hypothesis should contain observable and testable outcomes.

• Your hypothesis should be relevant to the research question.

6. What are the 2 types of hypothesis?

• Null hypotheses are used to test the claim that "there is no difference between two groups of data".

• Alternative hypotheses test the claim that "there is a difference between two data groups".

7. Difference between research question and research hypothesis?

A research question is a broad, open-ended question you will try to answer through your research. A hypothesis is a statement based on prior research or theory that you expect to be true due to your study. Example - Research question: What are the factors that influence the adoption of the new technology? Research hypothesis: There is a positive relationship between age, education and income level with the adoption of the new technology.

8. What is plural for hypothesis?

The plural of hypothesis is hypotheses. Here's an example of how it would be used in a statement, "Numerous well-considered hypotheses are presented in this part, and they are supported by tables and figures that are well-illustrated."

9. What is the red queen hypothesis?

The red queen hypothesis in evolutionary biology states that species must constantly evolve to avoid extinction because if they don't, they will be outcompeted by other species that are evolving. Leigh Van Valen first proposed it in 1973; since then, it has been tested and substantiated many times.

10. Who is known as the father of null hypothesis?

The father of the null hypothesis is Sir Ronald Fisher. He published a paper in 1925 that introduced the concept of null hypothesis testing, and he was also the first to use the term itself.

11. When to reject null hypothesis?

You need to find a significant difference between your two populations to reject the null hypothesis. You can determine that by running statistical tests such as an independent sample t-test or a dependent sample t-test. You should reject the null hypothesis if the p-value is less than 0.05.

You might also like

Consensus GPT vs. SciSpace GPT: Choose the Best GPT for Research

Literature Review and Theoretical Framework: Understanding the Differences

Types of Essays in Academic Writing - Quick Guide (2024)

• Privacy Policy

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.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

Research Process – Steps, Examples and Tips

Data Interpretation – Process, Methods and...

Theoretical Framework – Types, Examples and...

Thesis Statement – Examples, Writing Guide

Research Methods – Types, Examples and Guide

Ethical Considerations – Types, Examples and...

• +44 7897 053596
• [email protected]

Get an experienced writer start working

Review our examples before placing an order, learn how to draft academic papers, how to write a hypothesis in psychology | a step-by-step guide.

Plagiarism Remover Service That Can Help You Remove Plagiarism from Academic Papers Immediately

How to Write a Reference List? Practical Tips and Guidelines

Crafting your objectives, hypotheses, and supporting arguments for your research represents a seamless extension of the initial stages of brainstorming, reading, writing, and concept development that initiated your research journey.

Take a Look at What is Hypothesis in the Dissertation Here

A hypothesis is a testable proposition crucial for empirical validation. Before starting experiments or data collection, it's vital to formulate hypotheses, especially for assessing connections between variables.

Learn More About Writing Hypothesis in Research 

This article will discuss the multiple categories of hypotheses you may use, how to develop a strong hypothesis, how a hypothesis is utilized in psychology research, and, most importantly, how to write a hypothesis in psychology.

3-Step  Dissertation Process!

Get 3+ Topics

Dissertation Proposal

Get Final Dissertation

What is a hypothesis in psychology.

A hypothesis outlines your expectations for the conclusion of your research. It is a speculative, untested response to your research topic. You might need to develop several hypotheses for specific research projects that speak to various facets of your research issue.

Explore How to Write Research Project in an Expert Way 

A hypothesis should be founded on accepted ideas and a body of information; it shouldn't just be a wild guess. Additionally, it must be testable so that you may use scientific research techniques to confirm or disprove it (such as observations, experiments, and statistical data analysis).

Find New Ways to Analyse Data with SPSS

In psychology, the hypothesis is a theory-based, experimentally tested claim about a fact, behaviour, relation, or something similar that predicts a particular outcome under specific conditions or presumptions.

Explore More on Research Topics Related to Criminal Psychology 

In the scientific method, a hypothesis pertains to researchers' expectations regarding an experiment's outcome, whether in the realm of psychology or another discipline.

The following stages are part of the scientific method:

• Formulating a proposal
• Doing background investigation
• Form a supposition
• Making an experimental plan
• Gathering information
• Analyzing the outcomes
• Presenting the statistics

What are Variables?

Variables, or the components you are testing, are always discussed in the language of hypotheses. Anything seen can be a variable, including events, objects, ideas, etc.

Independent and dependent variables are the two different categories.

• Dependent variables are those you can only observe.
• Independent variables are those you can alter for your experiment.

Testimonials

Very satisfied students

This is our reason for working. We want to make all students happy, every day. Review us on Sitejabber

Types of Hypothesis

The hypothesis you employ depends on what you're investigating and analyzing. Here are several typical Types of Hypotheses you might consider utilizing:

1. Simple Hypothesis

This kind of hypothesis suggests a link between one independent and dependent variable.

2. Complex Hypothesis

This kind of hypothesis alludes to a connection between three or more variables, such as two independent variables and one dependent variable.

3. Null Hypothesis

According to the null hypothesis, the two variables under investigation have no connection (one variable does not affect the other). It claims that the findings are accidental and do not affect the hypothesis under study.

4. Alternative Hypothesis

The alternative hypothesis proposes a relationship between the two variables under study, suggesting that one variable influences the other. It asserts the significance of results in substantiating the concept analysis, emphasizing that they are not mere outcomes of chance.

5. Statistical Hypothesis

Using statistical analysis, this hypothesis evaluates a representative population sample and propagates the results to the population.

6. Empirical Hypothesis

An empirical hypothesis is one that is under examination at the moment; it is often referred to as a "working hypothesis." Empirical hypotheses are dependent on actual data, contrasting logical hypotheses.

7. Logical Hypothesis

Without any research or supporting information, this hypothesis implies a link between the variables.

Developing a Hypothesis

• A hypothesis is a proposed explanation for a phenomenon.
• A hypothesis must be testable and falsifiable.
• A hypothesis must be based on observable evidence.
• A hypothesis should be simple and thrifty.
• A hypothesis should be able to be supported or refuted by data.
• A hypothesis should make predictions that can be tested by experimentation.

How to Write a Hypothesis in Psychology?

The question of how to write a hypothesis for research must start with a question you really want to answer through your research input. 

Start by Asking a Question

A good hypothesis comes from a research question you want to answer. Your question should be short, clear, and fit what you can study.

Look closely at how things work. Find out where things come from that you see happening around you. If you can, pick a topic you find interesting for your research. This will make you naturally want to learn more about it.

Do Initial Research

Assemble some background data about the topic. The amount of background knowledge you require will depend on the task at hand. It can involve reading books, or it might only include running a short web search. Collect only the information you require to confirm or refute your hypothesis independently; you are not required to prove or invalidate it at the time.

Explain Your Variables

At this point, you can create a conceptual framework to specify the variables you'll examine and the interactions you believe exist between them. More complicated constructions may occasionally need to be operationalized.

Collect Evidence to Support Your Hypothesis

A hypothesis only serves as a framework to achieve a conclusion. You can start conducting experiments after defining your hypothesis and specifying your variables. It's ideal for gathering facts supporting your theory, but don't be concerned if your research disproves it; it's all a part of the scientific procedure.

Create Your Hypothesis

Now that you know what to anticipate, you can prepare yourself. Your initial response should be expressed in one clear, crisp sentence.

Revise and Refine

Ensure your hypothesis is evaluated and explicit. There are many ways to formulate a hypothesis, but it must include the following, and all terminology used must have precise definitions:

• The essential and relevant variables
• The particular group under observation
• The anticipated conclusion of the research or analysis

Methods of Collecting Data for Hypothesis

A researcher should choose a research plan and begin data collection after developing a scientific hypothesis. The research methodology mostly depends on the actual content under examination. Descriptive research and experimental research are the two fundamental sub-types of research methodology.

1. Descriptive Research

Descriptive research techniques, including case studies, naturalistic observations, and surveys, are frequently employed when it would be impractical or inconvenient to experiment. These techniques work well when describing several facets of behaviour or psychological phenomena.

2. Experimental Research

Experimental methods are employed to demonstrate the causal connections between the variables. In an experiment, a researcher deliberately modifies a critical variable (the independent variable) and evaluates the impact on a different variable (the dependent variable).

Finally, you should document your results in a research paper for others to access. It calls for some writing experience, a very distinct set of skills from experiments. The best way to learn how to write a hypothesis in Psychology is to read, research, and practice. Drop us a message for free samples of our written and published hypotheses in Psychology. 

Get an Immediate Response

Discuss your requirments with our writers

Get 3+ Free Dissertation Topics within 24 hours?

Your Number

Academic Level Select Academic Level Undergraduate Masters PhD

Area of Research

Related posts

How to Write a Reaction Paper: Format, Template, & Examples

What Is a Covariate? Its Role in Statistical Modeling

What is Conventions in Writing | Definition, Importance & Examples

Comments are closed.

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

Overview of the Scientific Method

10 Developing a Hypothesis

Learning objectives.

• Distinguish between a theory and a hypothesis.
• Discover how theories are used to generate hypotheses and how the results of studies can be used to further inform theories.
• Understand the characteristics of a good hypothesis.

Theories and Hypotheses

Before describing how to develop a hypothesis, it is important to distinguish between a theory and a hypothesis. A  theory  is a coherent explanation or interpretation of one or more phenomena. Although theories can take a variety of forms, one thing they have in common is that they go beyond the phenomena they explain by including variables, structures, processes, functions, or organizing principles that have not been observed directly. Consider, for example, Zajonc’s theory of social facilitation and social inhibition (1965) [1] . He proposed that being watched by others while performing a task creates a general state of physiological arousal, which increases the likelihood of the dominant (most likely) response. So for highly practiced tasks, being watched increases the tendency to make correct responses, but for relatively unpracticed tasks, being watched increases the tendency to make incorrect responses. Notice that this theory—which has come to be called drive theory—provides an explanation of both social facilitation and social inhibition that goes beyond the phenomena themselves by including concepts such as “arousal” and “dominant response,” along with processes such as the effect of arousal on the dominant response.

Outside of science, referring to an idea as a theory often implies that it is untested—perhaps no more than a wild guess. In science, however, the term theory has no such implication. A theory is simply an explanation or interpretation of a set of phenomena. It can be untested, but it can also be extensively tested, well supported, and accepted as an accurate description of the world by the scientific community. The theory of evolution by natural selection, for example, is a theory because it is an explanation of the diversity of life on earth—not because it is untested or unsupported by scientific research. On the contrary, the evidence for this theory is overwhelmingly positive and nearly all scientists accept its basic assumptions as accurate. Similarly, the “germ theory” of disease is a theory because it is an explanation of the origin of various diseases, not because there is any doubt that many diseases are caused by microorganisms that infect the body.

A  hypothesis , on the other hand, is a specific prediction about a new phenomenon that should be observed if a particular theory is accurate. It is an explanation that relies on just a few key concepts. Hypotheses are often specific predictions about what will happen in a particular study. They are developed by considering existing evidence and using reasoning to infer what will happen in the specific context of interest. Hypotheses are often but not always derived from theories. So a hypothesis is often a prediction based on a theory but some hypotheses are a-theoretical and only after a set of observations have been made, is a theory developed. This is because theories are broad in nature and they explain larger bodies of data. So if our research question is really original then we may need to collect some data and make some observations before we can develop a broader theory.

Theories and hypotheses always have this  if-then  relationship. “ If   drive theory is correct,  then  cockroaches should run through a straight runway faster, and a branching runway more slowly, when other cockroaches are present.” Although hypotheses are usually expressed as statements, they can always be rephrased as questions. “Do cockroaches run through a straight runway faster when other cockroaches are present?” Thus deriving hypotheses from theories is an excellent way of generating interesting research questions.

But how do researchers derive hypotheses from theories? One way is to generate a research question using the techniques discussed in this chapter  and then ask whether any theory implies an answer to that question. For example, you might wonder whether expressive writing about positive experiences improves health as much as expressive writing about traumatic experiences. Although this  question  is an interesting one  on its own, you might then ask whether the habituation theory—the idea that expressive writing causes people to habituate to negative thoughts and feelings—implies an answer. In this case, it seems clear that if the habituation theory is correct, then expressive writing about positive experiences should not be effective because it would not cause people to habituate to negative thoughts and feelings. A second way to derive hypotheses from theories is to focus on some component of the theory that has not yet been directly observed. For example, a researcher could focus on the process of habituation—perhaps hypothesizing that people should show fewer signs of emotional distress with each new writing session.

Among the very best hypotheses are those that distinguish between competing theories. For example, Norbert Schwarz and his colleagues considered two theories of how people make judgments about themselves, such as how assertive they are (Schwarz et al., 1991) [2] . Both theories held that such judgments are based on relevant examples that people bring to mind. However, one theory was that people base their judgments on the  number  of examples they bring to mind and the other was that people base their judgments on how  easily  they bring those examples to mind. To test these theories, the researchers asked people to recall either six times when they were assertive (which is easy for most people) or 12 times (which is difficult for most people). Then they asked them to judge their own assertiveness. Note that the number-of-examples theory implies that people who recalled 12 examples should judge themselves to be more assertive because they recalled more examples, but the ease-of-examples theory implies that participants who recalled six examples should judge themselves as more assertive because recalling the examples was easier. Thus the two theories made opposite predictions so that only one of the predictions could be confirmed. The surprising result was that participants who recalled fewer examples judged themselves to be more assertive—providing particularly convincing evidence in favor of the ease-of-retrieval theory over the number-of-examples theory.

Theory Testing

The primary way that scientific researchers use theories is sometimes called the hypothetico-deductive method  (although this term is much more likely to be used by philosophers of science than by scientists themselves). Researchers begin with a set of phenomena and either construct a theory to explain or interpret them or choose an existing theory to work with. They then make a prediction about some new phenomenon that should be observed if the theory is correct. Again, this prediction is called a hypothesis. The researchers then conduct an empirical study to test the hypothesis. Finally, they reevaluate the theory in light of the new results and revise it if necessary. This process is usually conceptualized as a cycle because the researchers can then derive a new hypothesis from the revised theory, conduct a new empirical study to test the hypothesis, and so on. As  Figure 2.3  shows, this approach meshes nicely with the model of scientific research in psychology presented earlier in the textbook—creating a more detailed model of “theoretically motivated” or “theory-driven” research.

As an example, let us consider Zajonc’s research on social facilitation and inhibition. He started with a somewhat contradictory pattern of results from the research literature. He then constructed his drive theory, according to which being watched by others while performing a task causes physiological arousal, which increases an organism’s tendency to make the dominant response. This theory predicts social facilitation for well-learned tasks and social inhibition for poorly learned tasks. He now had a theory that organized previous results in a meaningful way—but he still needed to test it. He hypothesized that if his theory was correct, he should observe that the presence of others improves performance in a simple laboratory task but inhibits performance in a difficult version of the very same laboratory task. To test this hypothesis, one of the studies he conducted used cockroaches as subjects (Zajonc, Heingartner, & Herman, 1969) [3] . The cockroaches ran either down a straight runway (an easy task for a cockroach) or through a cross-shaped maze (a difficult task for a cockroach) to escape into a dark chamber when a light was shined on them. They did this either while alone or in the presence of other cockroaches in clear plastic “audience boxes.” Zajonc found that cockroaches in the straight runway reached their goal more quickly in the presence of other cockroaches, but cockroaches in the cross-shaped maze reached their goal more slowly when they were in the presence of other cockroaches. Thus he confirmed his hypothesis and provided support for his drive theory. (Zajonc also showed that drive theory existed in humans [Zajonc & Sales, 1966] [4] in many other studies afterward).

Incorporating Theory into Your Research

When you write your research report or plan your presentation, be aware that there are two basic ways that researchers usually include theory. The first is to raise a research question, answer that question by conducting a new study, and then offer one or more theories (usually more) to explain or interpret the results. This format works well for applied research questions and for research questions that existing theories do not address. The second way is to describe one or more existing theories, derive a hypothesis from one of those theories, test the hypothesis in a new study, and finally reevaluate the theory. This format works well when there is an existing theory that addresses the research question—especially if the resulting hypothesis is surprising or conflicts with a hypothesis derived from a different theory.

To use theories in your research will not only give you guidance in coming up with experiment ideas and possible projects, but it lends legitimacy to your work. Psychologists have been interested in a variety of human behaviors and have developed many theories along the way. Using established theories will help you break new ground as a researcher, not limit you from developing your own ideas.

Characteristics of a Good Hypothesis

There are three general characteristics of a good hypothesis. First, a good hypothesis must be testable and falsifiable . We must be able to test the hypothesis using the methods of science and if you’ll recall Popper’s falsifiability criterion, it must be possible to gather evidence that will disconfirm the hypothesis if it is indeed false. Second, a good hypothesis must be logical. As described above, hypotheses are more than just a random guess. Hypotheses should be informed by previous theories or observations and logical reasoning. Typically, we begin with a broad and general theory and use  deductive reasoning to generate a more specific hypothesis to test based on that theory. Occasionally, however, when there is no theory to inform our hypothesis, we use  inductive reasoning  which involves using specific observations or research findings to form a more general hypothesis. Finally, the hypothesis should be positive. That is, the hypothesis should make a positive statement about the existence of a relationship or effect, rather than a statement that a relationship or effect does not exist. As scientists, we don’t set out to show that relationships do not exist or that effects do not occur so our hypotheses should not be worded in a way to suggest that an effect or relationship does not exist. The nature of science is to assume that something does not exist and then seek to find evidence to prove this wrong, to show that it really does exist. That may seem backward to you but that is the nature of the scientific method. The underlying reason for this is beyond the scope of this chapter but it has to do with statistical theory.

• Zajonc, R. B. (1965). Social facilitation.  Science, 149 , 269–274 ↵
• Schwarz, N., Bless, H., Strack, F., Klumpp, G., Rittenauer-Schatka, H., & Simons, A. (1991). Ease of retrieval as information: Another look at the availability heuristic.  Journal of Personality and Social Psychology, 61 , 195–202. ↵
• Zajonc, R. B., Heingartner, A., & Herman, E. M. (1969). Social enhancement and impairment of performance in the cockroach.  Journal of Personality and Social Psychology, 13 , 83–92. ↵
• Zajonc, R.B. & Sales, S.M. (1966). Social facilitation of dominant and subordinate responses. Journal of Experimental Social Psychology, 2 , 160-168. ↵

A coherent explanation or interpretation of one or more phenomena.

A specific prediction about a new phenomenon that should be observed if a particular theory is accurate.

A cyclical process of theory development, starting with an observed phenomenon, then developing or using a theory to make a specific prediction of what should happen if that theory is correct, testing that prediction, refining the theory in light of the findings, and using that refined theory to develop new hypotheses, and so on.

The ability to test the hypothesis using the methods of science and the possibility to gather evidence that will disconfirm the hypothesis if it is indeed false.

Research Methods in Psychology Copyright © 2019 by Rajiv S. Jhangiani, I-Chant A. Chiang, Carrie Cuttler, & Dana C. Leighton is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

Share This Book

Write For Us

We Are Constantly Looking For Writers And Contributors To Help Us Create Great Content For Our Blog Visitors.

What is a Hypothesis in Psychology?

A hypothesis in psychology is a specific, testable statement that proposes a relationship between variables or predicts an outcome in a research study. It is an educated guess or a tentative explanation for a phenomenon that researchers seek to investigate. Hypotheses help researchers narrow down their focus, establish the direction of their study, and evaluate the data collected.

Steps to Write a Hypothesis in Psychology

Identify the Research Topic:

To begin crafting a hypothesis, you first need a clear understanding of your research topic. What psychological phenomenon or aspect are you interested in exploring? Ensure that your research topic is well-defined and relevant to the field of psychology.

Review Existing Literature:

Before formulating your hypothesis, conduct a thorough review of existing literature on your chosen topic. This step is essential for understanding the current state of knowledge, identifying gaps or unanswered questions, and ensuring that your research contributes to the field.

Choose Your Variables:

Identify the variables involved in your study. Variables are factors or characteristics that can change or vary, and they are central to hypothesis formulation. In psychology, you typically have an independent variable (the factor you manipulate) and a dependent variable (the outcome you measure).

Formulate a Testable Hypothesis:

Your hypothesis should be clear, concise, and testable. It should state a specific prediction about the relationship between the independent and dependent variables. There are two main types of hypotheses in psychology:

Null Hypothesis (H0): This hypothesis states that there is no significant relationship or effect between the variables. It often represents the default assumption in research.

Alternative Hypothesis (H1 or Ha): This hypothesis proposes a specific relationship or effect between the variables. It is the hypothesis you aim to support with evidence.

For example, if you were studying the effect of sleep duration on academic performance, your null hypothesis might be: "There is no significant difference in academic performance between students who sleep 6 hours or less per night and those who sleep 7 hours or more per night." Your alternative hypothesis could be: "Students who sleep 7 hours or more per night will have significantly better academic performance than those who sleep 6 hours or less per night."

Specify the Direction of the Relationship:

In some cases, it's important to specify the expected direction of the relationship between variables. Will the independent variable have a positive or negative effect on the dependent variable? This adds depth to your hypothesis and makes it more precise.

Ensure Testability:

Your hypothesis should be testable through empirical research. This means you must be able to collect data and conduct experiments or surveys to either support or reject your hypothesis. Consider the feasibility of testing your hypothesis within the constraints of your study.

Make Predictions:

Your hypothesis should make clear, specific predictions about the expected outcomes of your research. These predictions guide the data collection and analysis process.

Revise and Refine:

It's common to revise and refine your hypothesis as you progress in your research. As you collect and analyze data, you may gain a better understanding of the relationship between variables and adjust your hypothesis accordingly.

Writing a hypothesis in psychology is a fundamental skill for A-Level students, as it forms the basis of scientific inquiry in the field. A well-constructed hypothesis should be based on a thorough review of existing literature, specify variables and their relationships, and be testable through empirical research. Remember that hypotheses can evolve as your research progresses, so stay open to revising and refining them as needed. By following these steps and maintaining a scientific approach, you'll be well-equipped to conduct meaningful research in the field of psychology at the A-Level.

What is The Null Hypothesis & When Do You Reject The Null Hypothesis

Julia Simkus

Editor at Simply Psychology

BA (Hons) Psychology, Princeton University

Julia Simkus is a graduate of Princeton University with a Bachelor of Arts in Psychology. She is currently studying for a Master's Degree in Counseling for Mental Health and Wellness in September 2023. Julia's research has been published in peer reviewed journals.

Learn about our Editorial Process

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

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

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

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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

On This Page:

A null hypothesis is a statistical concept suggesting no significant difference or relationship between measured variables. It’s the default assumption unless empirical evidence proves otherwise.

The null hypothesis states no relationship exists between the two variables being studied (i.e., one variable does not affect the other).

The null hypothesis is the statement that a researcher or an investigator wants to disprove.

Testing the null hypothesis can tell you whether your results are due to the effects of manipulating ​ the dependent variable or due to random chance. 

How to Write a Null Hypothesis

Null hypotheses (H0) start as research questions that the investigator rephrases as statements indicating no effect or relationship between the independent and dependent variables.

It is a default position that your research aims to challenge or confirm.

For example, if studying the impact of exercise on weight loss, your null hypothesis might be:

There is no significant difference in weight loss between individuals who exercise daily and those who do not.

Examples of Null Hypotheses

When Do We Reject The Null Hypothesis? 

We reject the null hypothesis when the data provide strong enough evidence to conclude that it is likely incorrect. This often occurs when the p-value (probability of observing the data given the null hypothesis is true) is below a predetermined significance level.

If the collected data does not meet the expectation of the null hypothesis, a researcher can conclude that the data lacks sufficient evidence to back up the null hypothesis, and thus the null hypothesis is rejected. 

Rejecting the null hypothesis means that a relationship does exist between a set of variables and the effect is statistically significant ( p > 0.05).

If the data collected from the random sample is not statistically significance , then the null hypothesis will be accepted, and the researchers can conclude that there is no relationship between the variables. 

You need to perform a statistical test on your data in order to evaluate how consistent it is with the null hypothesis. A p-value is one statistical measurement used to validate a hypothesis against observed data.

Calculating the p-value is a critical part of null-hypothesis significance testing because it quantifies how strongly the sample data contradicts the null hypothesis.

The level of statistical significance is often expressed as a  p  -value between 0 and 1. The smaller the p-value, the stronger the evidence that you should reject the null hypothesis.

Usually, a researcher uses a confidence level of 95% or 99% (p-value of 0.05 or 0.01) as general guidelines to decide if you should reject or keep the null.

When your p-value is less than or equal to your significance level, you reject the null hypothesis.

In other words, smaller p-values are taken as stronger evidence against the null hypothesis. Conversely, when the p-value is greater than your significance level, you fail to reject the null hypothesis.

In this case, the sample data provides insufficient data to conclude that the effect exists in the population.

Because you can never know with complete certainty whether there is an effect in the population, your inferences about a population will sometimes be incorrect.

When you incorrectly reject the null hypothesis, it’s called a type I error. When you incorrectly fail to reject it, it’s called a type II error.

Why Do We Never Accept The Null Hypothesis?

The reason we do not say “accept the null” is because we are always assuming the null hypothesis is true and then conducting a study to see if there is evidence against it. And, even if we don’t find evidence against it, a null hypothesis is not accepted.

A lack of evidence only means that you haven’t proven that something exists. It does not prove that something doesn’t exist. 

It is risky to conclude that the null hypothesis is true merely because we did not find evidence to reject it. It is always possible that researchers elsewhere have disproved the null hypothesis, so we cannot accept it as true, but instead, we state that we failed to reject the null. 

One can either reject the null hypothesis, or fail to reject it, but can never accept it.

Why Do We Use The Null Hypothesis?

We can never prove with 100% certainty that a hypothesis is true; We can only collect evidence that supports a theory. However, testing a hypothesis can set the stage for rejecting or accepting this hypothesis within a certain confidence level.

The null hypothesis is useful because it can tell us whether the results of our study are due to random chance or the manipulation of a variable (with a certain level of confidence).

A null hypothesis is rejected if the measured data is significantly unlikely to have occurred and a null hypothesis is accepted if the observed outcome is consistent with the position held by the null hypothesis.

Rejecting the null hypothesis sets the stage for further experimentation to see if a relationship between two variables exists. 

Hypothesis testing is a critical part of the scientific method as it helps decide whether the results of a research study support a particular theory about a given population. Hypothesis testing is a systematic way of backing up researchers’ predictions with statistical analysis.

It helps provide sufficient statistical evidence that either favors or rejects a certain hypothesis about the population parameter. 

Purpose of a Null Hypothesis 

• The primary purpose of the null hypothesis is to disprove an assumption. 
• Whether rejected or accepted, the null hypothesis can help further progress a theory in many scientific cases.
• A null hypothesis can be used to ascertain how consistent the outcomes of multiple studies are.

Do you always need both a Null Hypothesis and an Alternative Hypothesis?

The null (H0) and alternative (Ha or H1) hypotheses are two competing claims that describe the effect of the independent variable on the dependent variable. They are mutually exclusive, which means that only one of the two hypotheses can be true. 

While the null hypothesis states that there is no effect in the population, an alternative hypothesis states that there is statistical significance between two variables. 

The goal of hypothesis testing is to make inferences about a population based on a sample. In order to undertake hypothesis testing, you must express your research hypothesis as a null and alternative hypothesis. Both hypotheses are required to cover every possible outcome of the study. 

What is the difference between a null hypothesis and an alternative hypothesis?

The alternative hypothesis is the complement to the null hypothesis. The null hypothesis states that there is no effect or no relationship between variables, while the alternative hypothesis claims that there is an effect or relationship in the population.

It is the claim that you expect or hope will be true. The null hypothesis and the alternative hypothesis are always mutually exclusive, meaning that only one can be true at a time.

What are some problems with the null hypothesis?

One major problem with the null hypothesis is that researchers typically will assume that accepting the null is a failure of the experiment. However, accepting or rejecting any hypothesis is a positive result. Even if the null is not refuted, the researchers will still learn something new.

Why can a null hypothesis not be accepted?

We can either reject or fail to reject a null hypothesis, but never accept it. If your test fails to detect an effect, this is not proof that the effect doesn’t exist. It just means that your sample did not have enough evidence to conclude that it exists.

We can’t accept a null hypothesis because a lack of evidence does not prove something that does not exist. Instead, we fail to reject it.

Failing to reject the null indicates that the sample did not provide sufficient enough evidence to conclude that an effect exists.

If the p-value is greater than the significance level, then you fail to reject the null hypothesis.

Is a null hypothesis directional or non-directional?

A hypothesis test can either contain an alternative directional hypothesis or a non-directional alternative hypothesis. A directional hypothesis is one that contains the less than (“<“) or greater than (“>”) sign.

A nondirectional hypothesis contains the not equal sign (“≠”).  However, a null hypothesis is neither directional nor non-directional.

A null hypothesis is a prediction that there will be no change, relationship, or difference between two variables.

The directional hypothesis or nondirectional hypothesis would then be considered alternative hypotheses to the null hypothesis.

Gill, J. (1999). The insignificance of null hypothesis significance testing.  Political research quarterly ,  52 (3), 647-674.

Krueger, J. (2001). Null hypothesis significance testing: On the survival of a flawed method.  American Psychologist ,  56 (1), 16.

Masson, M. E. (2011). A tutorial on a practical Bayesian alternative to null-hypothesis significance testing.  Behavior research methods ,  43 , 679-690.

Nickerson, R. S. (2000). Null hypothesis significance testing: a review of an old and continuing controversy.  Psychological methods ,  5 (2), 241.

Rozeboom, W. W. (1960). The fallacy of the null-hypothesis significance test.  Psychological bulletin ,  57 (5), 416.

Live revision! Join us for our free exam revision livestreams Watch now →

Reference Library

Collections

• See what's new
• All Resources
• Student Resources
• Assessment Resources
• Teaching Resources
• CPD Courses
• Livestreams

Study notes, videos, interactive activities and more!

Psychology news, insights and enrichment

Currated collections of free resources

Browse resources by topic

• All Psychology Resources

Resource Selections

Currated lists of resources

A hypothesis is a testable prediction about the variables in a study. The hypothesis should always contain the independent variable (IV) and the dependent variable (DV). A hypothesis can be directional (one-tailed) or non-directional (two-tailed).

• Share on Facebook
• Share on Twitter
• Share by Email

Example Answers for Research Methods: A Level Psychology, Paper 2, June 2019 (AQA)

Exam Support

Research Methods: MCQ Revision Test 1 for AQA A Level Psychology

Topic Videos

Example Answers for Research Methods: A Level Psychology, Paper 2, June 2018 (AQA)

Example answer for question 14 paper 2: as psychology, june 2017 (aqa), a level psychology topic quiz - research methods.

Quizzes & Activities

Our subjects

• › Criminology
• › Economics
• › Geography
• › Health & Social Care
• › Psychology
• › Sociology
• › Teaching & learning resources
• › Student revision workshops
• › Online student courses
• › CPD for teachers
• › Livestreams
• › Teaching jobs

Boston House, 214 High Street, Boston Spa, West Yorkshire, LS23 6AD Tel: 01937 848885

• › Contact us
• › Terms of use
• › Privacy & cookies

© 2002-2024 Tutor2u Limited. Company Reg no: 04489574. VAT reg no 816865400.

AI Generator

When doing a research action plan students in school would know that the first thing to do is to know your topic well enough. From expecting science projects to work based on your predictions and the results that may have been quite the opposite from how you depicted them. This also rings true in businesses. There is a term for that and it is often associated with the subject Science, but can also be associated with business . Scientific method  or a hypothesis.

What Is a Hypothesis?

A hypothesis is a scientific wild guess, a prediction in research . A wild guess, a say from someone without any known proof.  A hypothesis can also mean a scientific, educated guess that most scientists and researchers do before planning out or doing experiments to check if their guesses or their scientific ideas based on their topics are exact or correct.

Hypothesis Format

A well-structured hypothesis is crucial for guiding scientific research. Here’s a detailed format for writing a hypothesis, along with examples for each step:

1. Start with a Research Question

Before writing a hypothesis, begin with a clear and concise research question . This question identifies the focus of your study.

Example Research Question: Does the amount of daily exercise affect weight loss?

2. Identify the Variables

Identify the independent and dependent variables in your research question.

• Independent Variable: The variable you manipulate (e.g., amount of daily exercise).
• Dependent Variable: The variable you measure (e.g., weight loss).

3. Formulate the Hypothesis

Use the identified variables to create a testable statement . This statement should clearly express the expected relationship between the variables.

• If [independent variable], then [dependent variable].
• [Independent variable] will [effect] [dependent variable].

Directional vs. Non-Directional Hypothesis:

• Specifies the direction of the expected relationship.
• Does not specify the direction of the expected relationship, only that a relationship exists.

4. Example Hypotheses Using the Format

Research question: does caffeine affect cognitive performance, if-then statement:.

• Example: If individuals consume caffeine, then their cognitive performance will improve.

Direct Statement:

• Example: Caffeine consumption will improve cognitive performance.

Null Hypothesis (H0):

• Example: There is no significant effect of caffeine consumption on cognitive performance.

Alternative Hypothesis (H1):

• Example: There is a significant effect of caffeine consumption on cognitive performance.

Directional Hypothesis:

Non-directional hypothesis:.

• Example: There is a relationship between caffeine consumption and cognitive performance.

5. Refining the Hypothesis

Ensure that your hypothesis is specific, measurable, and testable. Avoid vague terms and focus on a single independent and dependent variable.

Hypothesis Examples in Research

A hypothesis is a statement that predicts the relationship between variables. It serves as a foundation for research by providing a clear focus and direction for experiments and data analysis . Here are examples of hypotheses from various fields of research:

Research Question:

Does sunlight exposure affect plant growth?

Hypotheses:

• Null Hypothesis (H0): There is no significant difference in plant growth between plants exposed to sunlight and those kept in the shade.
• Alternative Hypothesis (H1): Plants exposed to sunlight grow taller than those kept in the shade.
• Directional Hypothesis: Increased sunlight exposure will lead to increased plant growth.
• If-Then Statement: If plants are exposed to more sunlight, then they will grow taller.

2. Psychology

Does sleep duration affect memory retention?

• Null Hypothesis (H0): There is no significant difference in memory retention between individuals who sleep for 8 hours and those who sleep for 4 hours.
• Alternative Hypothesis (H1): Individuals who sleep for 8 hours will have better memory retention than those who sleep for 4 hours.
• Directional Hypothesis: Longer sleep duration will improve memory retention.
• If-Then Statement: If individuals sleep for 8 hours, then their memory retention will improve compared to those who sleep for 4 hours.

3. Education

Do interactive teaching methods improve student engagement?

• Null Hypothesis (H0): There is no significant difference in student engagement between interactive teaching methods and traditional lecture-based methods.
• Alternative Hypothesis (H1): Interactive teaching methods result in higher student engagement compared to traditional lecture-based methods.
• Directional Hypothesis: Interactive teaching methods will increase student engagement.
• If-Then Statement: If teachers use interactive teaching methods, then student engagement will increase.

4. Medicine

Does a new drug reduce blood pressure more effectively than the standard medication?

• Null Hypothesis (H0): There is no significant difference in blood pressure reduction between the new drug and the standard medication.
• Alternative Hypothesis (H1): The new drug reduces blood pressure more effectively than the standard medication.
• Directional Hypothesis: The new drug will reduce blood pressure more than the standard medication.
• If-Then Statement: If patients take the new drug, then their blood pressure will decrease more than if they take the standard medication.

5. Sociology

Does socioeconomic status affect access to higher education?

• Null Hypothesis (H0): There is no significant relationship between socioeconomic status and access to higher education.
• Alternative Hypothesis (H1): Higher socioeconomic status is associated with greater access to higher education.
• Directional Hypothesis: Individuals with higher socioeconomic status will have greater access to higher education.
• If-Then Statement: If individuals have a higher socioeconomic status, then they will have greater access to higher education.

Hypothesis Examples in Psychology

Psychology research often explores the relationships between various cognitive, behavioral, and emotional variables. Here are some well-structured hypothesis examples in psychology:

1. Sleep Duration and Memory Retention

• Non-Directional Hypothesis: There is a relationship between sleep duration and memory retention.

2. Exercise and Anxiety Levels

Does regular exercise reduce anxiety levels?

• Null Hypothesis (H0): There is no significant difference in anxiety levels between individuals who exercise regularly and those who do not.
• Alternative Hypothesis (H1): Individuals who exercise regularly will have lower anxiety levels than those who do not.
• Directional Hypothesis: Regular exercise will decrease anxiety levels.
• Non-Directional Hypothesis: There is a relationship between regular exercise and anxiety levels.
• If-Then Statement: If individuals exercise regularly, then their anxiety levels will decrease.

3. Social Media Usage and Self-Esteem

Does social media usage affect self-esteem in teenagers?

• Null Hypothesis (H0): There is no significant relationship between social media usage and self-esteem in teenagers.
• Alternative Hypothesis (H1): High social media usage is associated with lower self-esteem in teenagers.
• Directional Hypothesis: Increased social media usage will decrease self-esteem in teenagers.
• Non-Directional Hypothesis: There is a relationship between social media usage and self-esteem in teenagers.
• If-Then Statement: If teenagers spend more time on social media, then their self-esteem will decrease.

4. Cognitive Behavioral Therapy (CBT) and Depression

Is Cognitive Behavioral Therapy (CBT) effective in reducing symptoms of depression?

• Null Hypothesis (H0): There is no significant difference in depression symptoms between individuals who undergo CBT and those who do not.
• Alternative Hypothesis (H1): Individuals who undergo CBT will experience a greater reduction in depression symptoms than those who do not.
• Directional Hypothesis: CBT will reduce symptoms of depression.
• Non-Directional Hypothesis: There is a relationship between undergoing CBT and reduction in depression symptoms.
• If-Then Statement: If individuals undergo CBT, then their symptoms of depression will decrease.

5. Parental Involvement and Academic Achievement

Does parental involvement influence academic achievement in children?

• Null Hypothesis (H0): There is no significant relationship between parental involvement and academic achievement in children.
• Alternative Hypothesis (H1): Higher levels of parental involvement are associated with higher academic achievement in children.
• Directional Hypothesis: Increased parental involvement will improve academic achievement in children.
• Non-Directional Hypothesis: There is a relationship between parental involvement and academic achievement in children.
• If-Then Statement: If parents are more involved in their children’s education, then their children will achieve higher academic success.

Hypothesis Examples in Science

Scientific research often involves creating hypotheses to test the relationships between variables. Here are some well-structured hypothesis examples from various fields of science:

1. Biology: Sunlight and Plant Growth

• Non-Directional Hypothesis: There is a relationship between sunlight exposure and plant growth.

2. Chemistry: Temperature and Reaction Rate

Does temperature affect the rate of a chemical reaction?

• Null Hypothesis (H0): There is no significant difference in the reaction rate of a chemical reaction at different temperatures.
• Alternative Hypothesis (H1): Increasing the temperature will increase the reaction rate.
• Directional Hypothesis: Higher temperatures will increase the reaction rate.
• Non-Directional Hypothesis: There is a relationship between temperature and the reaction rate.
• If-Then Statement: If the temperature of a reaction increases, then the reaction rate will increase.

3. Physics: Mass and Free Fall Speed

Does the mass of an object affect its speed when falling?

• Null Hypothesis (H0): There is no significant difference in the falling speed of objects with different masses.
• Alternative Hypothesis (H1): Objects with greater mass fall faster than those with lesser mass.
• Directional Hypothesis: Heavier objects will fall faster than lighter objects.
• Non-Directional Hypothesis: There is a relationship between the mass of an object and its falling speed.
• If-Then Statement: If an object’s mass increases, then its falling speed will increase.

4. Environmental Science: Fertilizers and Water Quality

Do chemical fertilizers affect water quality in nearby lakes?

• Null Hypothesis (H0): There is no significant effect of chemical fertilizers on the water quality of nearby lakes.
• Alternative Hypothesis (H1): Chemical fertilizers negatively affect the water quality of nearby lakes.
• Directional Hypothesis: The use of chemical fertilizers will decrease the water quality of nearby lakes.
• Non-Directional Hypothesis: There is a relationship between the use of chemical fertilizers and the water quality of nearby lakes.
• If-Then Statement: If chemical fertilizers are used, then the water quality in nearby lakes will decrease.

5. Earth Science: Soil Composition and Erosion Rate

Does soil composition affect the rate of erosion?

• Null Hypothesis (H0): There is no significant difference in the erosion rate of soils with different compositions.
• Alternative Hypothesis (H1): Soil composition affects the rate of erosion.
• Directional Hypothesis: Soils with higher clay content will erode more slowly than sandy soils.
• Non-Directional Hypothesis: There is a relationship between soil composition and the rate of erosion.
• If-Then Statement: If soil has a higher clay content, then its erosion rate will be lower compared to sandy soil.

Hypothesis Examples in Biology

In biology, hypotheses are used to explore relationships and effects within biological systems. Here are some well-structured hypothesis examples in various areas of biology:

1. Photosynthesis and Light Intensity

How does light intensity affect the rate of photosynthesis in plants?

• Null Hypothesis (H0): Light intensity has no significant effect on the rate of photosynthesis in plants.
• Alternative Hypothesis (H1): Light intensity significantly affects the rate of photosynthesis in plants.
• Directional Hypothesis: Increased light intensity will increase the rate of photosynthesis in plants.
• Non-Directional Hypothesis: There is a relationship between light intensity and the rate of photosynthesis in plants.
• If-Then Statement: If light intensity increases, then the rate of photosynthesis in plants will increase.

2. Temperature and Enzyme Activity

How does temperature affect the activity of the enzyme amylase?

• Null Hypothesis (H0): Temperature has no significant effect on the activity of the enzyme amylase.
• Alternative Hypothesis (H1): Temperature significantly affects the activity of the enzyme amylase.
• Directional Hypothesis: Increasing the temperature will increase the activity of the enzyme amylase up to an optimal point, after which activity will decrease.
• Non-Directional Hypothesis: There is a relationship between temperature and the activity of the enzyme amylase.
• If-Then Statement: If the temperature increases, then the activity of the enzyme amylase will increase up to an optimal temperature, after which it will decrease.

3. Nutrient Availability and Plant Growth

Does the availability of nutrients in soil affect the growth of plants?

• Null Hypothesis (H0): Nutrient availability has no significant effect on the growth of plants.
• Alternative Hypothesis (H1): Nutrient availability significantly affects the growth of plants.
• Directional Hypothesis: Increased nutrient availability will enhance plant growth.
• Non-Directional Hypothesis: There is a relationship between nutrient availability and plant growth.
• If-Then Statement: If nutrient availability in the soil increases, then the growth of plants will be enhanced.

4. Genetic Variation and Disease Resistance

Does genetic variation in a population affect its resistance to diseases?

• Null Hypothesis (H0): Genetic variation has no significant effect on disease resistance in a population.
• Alternative Hypothesis (H1): Genetic variation significantly affects disease resistance in a population.
• Directional Hypothesis: Populations with greater genetic variation will have higher resistance to diseases.
• Non-Directional Hypothesis: There is a relationship between genetic variation and disease resistance in a population.
• If-Then Statement: If a population has greater genetic variation, then its resistance to diseases will be higher.

5. Water pH and Aquatic Life Health

Does the pH level of water affect the health of aquatic life?

• Null Hypothesis (H0): The pH level of water has no significant effect on the health of aquatic life.
• Alternative Hypothesis (H1): The pH level of water significantly affects the health of aquatic life.
• Directional Hypothesis: Extreme pH levels (both high and low) will negatively affect the health of aquatic life.
• Non-Directional Hypothesis: There is a relationship between the pH level of water and the health of aquatic life.
• If-Then Statement: If the pH level of water is too high or too low, then the health of aquatic life will be negatively affected.

Hypothesis Examples in Sociology

In sociology, hypotheses are used to explore and explain social phenomena, behaviors, and relationships within societies. Here are some well-structured hypothesis examples in various areas of sociology:

1. Education and Social Mobility

Does access to higher education affect social mobility?

• Null Hypothesis (H0): Access to higher education has no significant effect on social mobility.
• Alternative Hypothesis (H1): Access to higher education significantly affects social mobility.
• Directional Hypothesis: Increased access to higher education will improve social mobility.
• Non-Directional Hypothesis: There is a relationship between access to higher education and social mobility.
• If-Then Statement: If individuals have increased access to higher education, then their social mobility will improve.

2. Income Inequality and Crime Rates

Does income inequality influence crime rates in urban areas?

• Null Hypothesis (H0): Income inequality has no significant effect on crime rates in urban areas.
• Alternative Hypothesis (H1): Income inequality significantly affects crime rates in urban areas.
• Directional Hypothesis: Higher income inequality will lead to higher crime rates in urban areas.
• Non-Directional Hypothesis: There is a relationship between income inequality and crime rates in urban areas.
• If-Then Statement: If income inequality increases, then crime rates in urban areas will increase.

3. Social Media Use and Social Interaction

Does the use of social media affect face-to-face social interactions among teenagers?

• Null Hypothesis (H0): The use of social media has no significant effect on face-to-face social interactions among teenagers.
• Alternative Hypothesis (H1): The use of social media significantly affects face-to-face social interactions among teenagers.
• Directional Hypothesis: Increased use of social media will decrease face-to-face social interactions among teenagers.
• Non-Directional Hypothesis: There is a relationship between the use of social media and face-to-face social interactions among teenagers.
• If-Then Statement: If teenagers use social media more frequently, then their face-to-face social interactions will decrease.

4. Gender Roles and Career Choices

Do traditional gender roles influence career choices among young adults?

• Null Hypothesis (H0): Traditional gender roles have no significant effect on career choices among young adults.
• Alternative Hypothesis (H1): Traditional gender roles significantly affect career choices among young adults.
• Directional Hypothesis: Adherence to traditional gender roles will limit career choices among young adults.
• Non-Directional Hypothesis: There is a relationship between traditional gender roles and career choices among young adults.
• If-Then Statement: If young adults adhere to traditional gender roles, then their career choices will be limited.

5. Cultural Diversity and Workplace Productivity

Does cultural diversity in the workplace affect productivity levels?

• Null Hypothesis (H0): Cultural diversity in the workplace has no significant effect on productivity levels.
• Alternative Hypothesis (H1): Cultural diversity in the workplace significantly affects productivity levels.
• Directional Hypothesis: Increased cultural diversity will improve productivity levels in the workplace.
• Non-Directional Hypothesis: There is a relationship between cultural diversity in the workplace and productivity levels.
• If-Then Statement: If the workplace has increased cultural diversity, then productivity levels will improve.

More Hypothesis Samples & Examples in PDF

1. research hypothesis.

2. Education Hypothesis

3. Basic Hypothesis

4. Hypothesis Statement Template

5. Hypothesis in PDF

6. Hypothesis Format

7. Hypothesis Examples

8. Simple Hypothesis

Types of Hypothesis

A hypothesis is a statement that can be tested and is often used in scientific research to propose a relationship between two or more variables. Understanding the different types of hypotheses is essential for conducting effective research. Below are the main types of hypotheses:

1. Null Hypothesis (H0)

The null hypothesis states that there is no relationship between the variables being studied. It assumes that any observed effect is due to chance. Researchers often aim to disprove the null hypothesis.

Example: There is no significant difference in test scores between students who study with music and those who study in silence.

2. Alternative Hypothesis (H1 or Ha)

The alternative hypothesis suggests that there is a relationship between the variables being studied. It is what researchers seek to prove.

Example: Students who study with music have higher test scores than those who study in silence.

3. Simple Hypothesis

A simple hypothesis predicts a relationship between a single independent variable and a single dependent variable.

Example: Increasing the amount of sunlight will increase the growth rate of plants.

4. Complex Hypothesis

A complex hypothesis predicts a relationship involving two or more independent variables and/or two or more dependent variables.

Example: Increasing sunlight and water will increase the growth rate and height of plants.

5. Directional Hypothesis

A directional hypothesis specifies the direction of the expected relationship between variables. It suggests whether the relationship is positive or negative.

Example: Students who study for more hours will score higher on exams.

6. Non-Directional Hypothesis

A non-directional hypothesis does not specify the direction of the relationship. It only states that a relationship exists.

Example: There is a difference in test scores between students who study with music and those who study in silence.

7. Statistical Hypothesis

A statistical hypothesis involves quantitative data and can be tested using statistical methods. It often includes both null and alternative hypotheses.

Example: The mean test scores of students who study with music are significantly different from those who study in silence.

8. Causal Hypothesis

A causal hypothesis proposes a cause-and-effect relationship between variables. It suggests that one variable causes a change in another.

Example: Smoking causes lung cancer.

9. Associative Hypothesis

An associative hypothesis suggests that variables are related but does not imply causation.

Example: There is an association between physical activity levels and body weight.

10. Research Hypothesis

A research hypothesis is a broad statement that serves as the foundation for the research study. It is often the same as the alternative hypothesis.

Example: Implementing a new teaching strategy will improve student engagement and performance.

How To Use Hypothesis for Research?

A hypothesis is a critical component of the research process, providing a clear direction for the study and forming the basis for drawing conclusions. Here’s a step-by-step guide on how to use a hypothesis in research:

1. Identify the Research Problem

Before formulating a hypothesis, clearly define the research problem or question. This step involves understanding what you aim to investigate and why it is significant.

Example: You want to study the impact of sleep on academic performance among college students.

2. Review Existing Literature

Conduct a thorough review of existing literature to understand what is already known about the topic. This helps in identifying gaps in knowledge and forming a basis for your hypothesis.

Example: Previous studies suggest a positive correlation between sleep duration and academic performance but lack specific data on college students.

Based on the research problem and literature review, formulate a clear and testable hypothesis. Ensure it is specific and relates directly to the variables being studied.

Types of Hypotheses:

• Null Hypothesis (H0): There is no significant relationship between sleep duration and academic performance among college students.
• Alternative Hypothesis (H1): There is a significant relationship between sleep duration and academic performance among college students.

4. Define Variables

Clearly define the independent and dependent variables involved in the hypothesis.

• Independent Variable: Sleep duration
• Dependent Variable: Academic performance (e.g., GPA)

5. Design the Study

Choose an appropriate research design to test the hypothesis. This could be experimental, correlational, or observational, depending on the nature of your research question.

Example: Conduct a correlational study to examine the relationship between sleep duration and GPA among college students.

6. Collect Data

Gather data through surveys, experiments, or secondary data sources. Ensure the data collection methods are reliable and valid to accurately test the hypothesis.

Example: Use a questionnaire to collect data on students’ sleep duration and their GPAs.

7. Analyze the Data

Use appropriate statistical methods to analyze the data. This step involves testing the hypothesis to determine whether to accept or reject the null hypothesis.

Example: Perform a Pearson correlation analysis to examine the relationship between sleep duration and GPA.

8. Interpret the Results

Interpret the results of the statistical analysis. Determine if the data supports the alternative hypothesis or if the null hypothesis cannot be rejected.

Example: If the analysis shows a significant positive correlation, you can reject the null hypothesis and accept the alternative hypothesis that sleep duration is related to academic performance.

9. Draw Conclusions

Draw conclusions based on the results of the hypothesis testing. Discuss the implications of the findings and how they contribute to the existing body of knowledge.

Example: Conclude that longer sleep duration is associated with higher GPA among college students and discuss potential implications for student health and academic policies.

10. Report and Share Findings

Write a detailed report or research paper presenting the hypothesis, methodology, results, and conclusions. Share your findings with the academic community or relevant stakeholders.

Example: Publish the study in a peer-reviewed journal or present it at an academic conference.

How to Write a Hypothesis?

Writing a hypothesis is a crucial step in the scientific method. A well-constructed hypothesis guides your research, helping you design experiments and analyze results. Here’s a step-by-step guide on how to write an effective hypothesis:

1. Understand the Research Question

Start by clearly understanding the research question or problem you want to address. This helps in formulating a focused hypothesis.

Example: How does sunlight exposure affect plant growth?

2. Conduct Preliminary Research

Review existing literature related to your research question. This helps in understanding what is already known and identifying gaps in knowledge.

Example: Studies show that plants generally grow better with more sunlight, but the optimal amount varies.

3. Identify Variables

Determine the independent and dependent variables for your study.

• Independent Variable: The factor you manipulate (e.g., sunlight exposure).
• Dependent Variable: The factor you measure (e.g., plant growth).

4. Formulate a Simple Hypothesis

A simple hypothesis involves one independent and one dependent variable. Clearly state the expected relationship between these variables.

Example: Increasing sunlight exposure will increase plant growth.

5. Choose the Type of Hypothesis

Decide whether your hypothesis will be null or alternative, directional or non-directional.

• Null Hypothesis (H0): There is no relationship between the variables.
• Alternative Hypothesis (H1): There is a relationship between the variables.
• Directional Hypothesis: Specifies the direction of the relationship.
• Non-Directional Hypothesis: Does not specify the direction.

Example of Directional Hypothesis: Plants exposed to more sunlight will grow taller than those exposed to less sunlight.

6. Ensure Testability

Make sure your hypothesis can be tested through experiments or observations. It should be measurable and falsifiable.

Example: Plants will be grown under different levels of sunlight, and their growth will be measured over time.

7. Write the Hypothesis

Write your hypothesis in a clear, concise, and specific manner. It should include the variables and the expected relationship between them.

Example: If plants are exposed to increased sunlight, then they will grow taller compared to plants that receive less sunlight.

8. Refine the Hypothesis

Ensure that your hypothesis is specific and narrow enough to be testable but broad enough to cover the scope of your research.

Example: If tomato plants are exposed to 8 hours of sunlight per day, then they will grow taller and produce more fruit compared to tomato plants exposed to 4 hours of sunlight per day.

How Do You Formulate a Hypothesis?

To formulate a hypothesis, identify the research question, review existing literature, define variables, and create a testable statement predicting the relationship between the variables.

What Is the Difference Between Null and Alternative Hypotheses?

The null hypothesis (H0) states there is no effect or relationship, while the alternative hypothesis (H1) proposes that there is an effect or relationship.

Why Is a Hypothesis Important in Research?

A hypothesis provides a clear focus for the study, guiding the research design, data collection, and analysis, ultimately helping to draw meaningful conclusions.

Can a Hypothesis Be Proven True?

A hypothesis cannot be proven true; it can only be supported or refuted through experimentation and analysis. Even if supported, it remains open to further testing.

What Makes a Good Hypothesis?

A good hypothesis is clear, concise, specific, testable, and based on existing knowledge. It should predict a relationship between variables that can be measured.

How Is a Hypothesis Tested?

A hypothesis is tested through experiments or observations, collecting and analyzing data to determine if the results support or refute the hypothesis.

What Are the Types of Hypotheses?

Types of hypotheses include null, alternative, simple, complex, directional, non-directional, statistical, causal, and associative.

What Is a Directional Hypothesis?

A directional hypothesis specifies the expected direction of the relationship between variables, indicating whether the effect will be positive or negative.

What Is a Non-Directional Hypothesis?

A non-directional hypothesis states that a relationship exists between variables but does not specify the direction of the relationship.

How Do You Refine a Hypothesis?

Refine a hypothesis by ensuring it is specific, measurable, and testable. Remove any vague terms and focus on a single independent and dependent variable.

Text prompt

• Instructive
• Professional

10 Examples of Public speaking

20 Examples of Gas lighting

Do you have a mental illness? Why some people answer ‘yes’, even if they haven’t been diagnosed

PhD Candidate at Melbourne School of Psychological Sciences, The University of Melbourne

Professor of Psychology, The University of Melbourne

Disclosure statement

Nick Haslam receives funding from the Australian Research Council.

Jesse Tse does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

University of Melbourne provides funding as a founding partner of The Conversation AU.

View all partners

Mental illnesses such as depression and anxiety disorders have become more prevalent, especially among young people . Demand for treatment is surging and prescriptions of some psychiatric medications have climbed.

These upswinging prevalence trends are paralleled by rising public attention to mental illness. Mental health messages saturate traditional and social media. Organisations and governments are developing awareness, prevention and treatment initiatives with growing urgency.

The mounting cultural focus on mental health has obvious benefits. It increases awareness, reduces stigma and promotes help-seeking.

However, it may also have costs. Critics worry social media sites are incubating mental illness and that ordinary unhappiness is being pathologised by the overuse of diagnostic concepts and “ therapy speak ”.

British psychologist Lucy Foulkes argues the trends for rising attention and prevalence are linked. Her “ prevalence inflation hypothesis ” proposes that increasing awareness of mental illness may lead some people to diagnose themselves inaccurately when they are experiencing relatively mild or transient problems.

Foulkes’ hypothesis implies that some people develop overly broad concepts of mental illness. Our research supports this view. In a new study, we show that concepts of mental illness have broadened in recent years – a phenomenon we call “ concept creep ” – and that people differ in the breadth of their concepts of mental illness.

Why do people self-diagnose mental illnesses?

In our new study , we examined whether people with broad concepts of mental illness are, in fact, more likely to self-diagnose.

We defined self-diagnosis as a person’s belief they have an illness, whether or not they have received the diagnosis from a professional. We assessed people as having a “broad concept of mental illness” if they judged a wide variety of experiences and behaviours to be disorders, including relatively mild conditions.

We asked a nationally representative sample of 474 American adults if they believed they had a mental disorder and if they had received a diagnosis from a health professional. We also asked about other possible contributing factors and demographics.

Mental illness was common in our sample: 42% reported they had a current self-diagnosed condition, a majority of whom had received it from a health professional.

Unsurprisingly, the strongest predictor of reporting a diagnosis was experiencing relatively severe distress.

The second most important factor after distress was having a broad concept of mental illness. When their levels of distress were the same, people with broad concepts were substantially more likely to report a current diagnosis.

The graph below illustrates this effect. It divides the sample by levels of distress and shows the proportion of people at each level who report a current diagnosis. People with broad concepts of mental illness (the highest quarter of the sample) are represented by the dark blue line. People with narrow concepts of mental illness (the lowest quarter of the sample) are represented by the light blue line. People with broad concepts were much more likely to report having a mental illness, especially when their distress was relatively high.

People with greater mental health literacy and less stigmatising attitudes were also more likely to report a diagnosis.

Two interesting further findings emerged from our study. People who self-diagnosed but had not received a professional diagnosis tended to have broader illness concepts than those who had.

In addition, younger and politically progressive people were more likely to report a diagnosis, consistent with some previous research , and held broader concepts of mental illness. Their tendency to hold these more expansive concepts partially explained their higher rates of diagnosis.

Why does it matter?

Our findings support the idea that expansive concepts of mental illness promote self-diagnosis and may thereby increase the apparent prevalence of mental ill health. People who have a lower threshold for defining distress as a disorder are more likely to identify themselves as having a mental illness.

Our findings do not directly show that people with broad concepts over-diagnose or those with narrow concepts under-diagnose. Nor do they prove that having broad concepts causes self-diagnosis or results in actual increases in mental illness. Nevertheless, the findings raise important concerns.

First, they suggest that rising mental health awareness may come at a cost . In addition to boosting mental health literacy it may increase the likelihood of people incorrectly identifying their problems as pathologies.

Inappropriate self-diagnosis can have adverse effects. Diagnostic labels may become identity-defining and self-limiting, as people come to believe their problems are enduring, hard-to-control aspects of who they are.

Second, unwarranted self-diagnosis may lead people experiencing relatively mild levels of distress to seek help that is unnecessary, inappropriate and ineffective. Recent Australian research found people with relatively mild distress who received psychotherapy worsened more often than they improved.

Third, these effects may be particularly problematic for young people. They are most liable to hold broad concepts of mental illness, in part due to social media consumption , and they experience mental ill health at relatively high and rising rates. Whether expansive concepts of illness play a role in the youth mental health crisis remains to be seen.

Ongoing cultural shifts are fostering increasingly expansive definitions of mental illness. These shifts are likely to have mixed blessings. By normalising mental illness they may help to remove its stigma. However, by pathologising some forms of everyday distress, they may have an unintended downside.

As we wrestle with the mental health crisis, it is crucial we find ways to increase awareness of mental ill health without inadvertently inflating it.

• Mental health
• Mental illness
• Overdiagnosis
• Health research
• New research, Australia New Zealand

Centre Director, Transformative Media Technologies

Stephen Knight Lecturer in Medieval Literature

Postdoctoral Research Fellowship

Social Media Producer

Dean (Head of School), Indigenous Knowledges

June 25, 2024

How Earth Went from a Sterile Rock to a Lush, Living Planet

From microbes to mammoths, life has transformed Earth into one big living system, says Ferris Jabr, author of Becoming Earth: How Our Planet Came to Life

By Allison Parshall

Ricardo Lima/Getty Images

According to the Gaia hypothesis, life created Earth—or at least the Earth we know today. This theory, first formulated by chemist James Lovelock in the 1970s, was a hit with the media and the public but wasn’t taken seriously by fellow scientists.

“The scientific community harshly criticized and ridiculed” the Gaia hypothesis, recounts Ferris Jabr , author of the new book Becoming Earth: How Our Planet Came to Life. That’s because scientists at the time tended to think of evolution as a one-way street; they thought that while the planet’s environment shaped life, life did not meaningfully shape its environment. Yet we know now that life left an indelible mark on the planet. The very air we breathe, for example, would not exist if cyanobacteria hadn’t begun spewing oxygen 2.4 billion years ago. Microbes carve subterranean caverns and transform barren rock into fertile soil. Large herbivores summon grasslands by trampling the ground. Life in the Amazon rainforest summons half of the rain that sustains it, making it a sort of “garden that waters itself,” Jabr writes.

We are now beginning to see that life has transformed its home planet in ways more diverse and wondrous than most of us could imagine. “This gives a sort of new, expansive truth to the trite phrase that we’ve heard over and over—that everything is interconnected,” says Jabr, a contributing editor at Scientific American . “Life is not just something that resides on the planet; it’s an extension of the planet. What we call ‘life’ is the matter of Earth, animated.”

On supporting science journalism

If you're enjoying this article, consider supporting our award-winning journalism by subscribing . By purchasing a subscription you are helping to ensure the future of impactful stories about the discoveries and ideas shaping our world today.

Scientific American spoke with Jabr about his skyscraping adventures in writing his new book, the incredible ways that life transformed our world and humanity’s responsibility in the face of climate catastrophe.

[ An edited transcript of the interview follows. ]

What was the first spark of this book?

Learning about the Amazon’s self-generated rain cycle was a huge moment for me. That was back in [journalism] graduate school, around 2010. I was writing a lot about plant behavior and communication and learning how much agency plants have in their environment and in their evolution. And I learned about the Amazon’s self-generated rain cycle. What astonished me was that it was not just about plants pulling water from the soil and moving it to the atmosphere [where it would then rain down]. The process also involved all of these bioaerosols—these tiny biological particles that were derived from plants and fungi and microbes and animals. It’s really the entire forest participating actively in the water cycle and changing weather throughout the continent.

That just made me think of the relationship between Earth and life in a totally different way. I was not used to thinking of life so profoundly transforming its environment and even changing the weather itself. And I wanted to know: What are the other examples of this phenomenon? How else has life dramatically transformed the planet?

There’s a strong sense of awe running through the whole book. Was there anything you learned in your research that left you particularly awestruck?

I remember learning that if you take a piece of chalk from the White Cliffs of Dover [on the coast of England] and look at it under a powerful microscope, you’ll see these tiny, bonelike pegs arranged in little archways. And that’s because the White Cliffs of Dover are made of the compacted remains of ancient single-celled ocean plankton that encased themselves in an intricate chalky exoskeleton. Most of the major chalk formations on the planet are made of the remains of tiny, ancient life—and therefore all the monuments we’ve ever made with limestone are made of plankton and other ancient sea creatures. That just really blew my mind. What I had previously seen as just huge pieces of rock were actually made by and of life. For me, that was very illustrative of this reciprocity of geology and biology, of earth and life.

You got to see some of that reciprocity up close in your travels and even got to climb a very tall tower to see the Amazon rainforest make its own rain. Can you tell me about that experience?

I learned about the Amazon Tall Tower Observatory [in Brazil] early on in my research, but I had to wait more than two years to get there because of the pandemic. And when I finally got there, I was standing at the bottom of this tower [in the middle of the rainforest] and looking up, and I was like, “Huh, I don’t know if I’m actually ready to climb this thing.” If you climb the Eiffel Tower, which is approximately the same height [at about 1,000 feet], it is wrapped in safety barriers. But this tower in the forest is as bare-bones as it can possibly be; there are huge gaps that your whole body could fit through. I was prepared to be very scared, but I found it exhilarating to see the forest from so many different levels. You get to the very top and you really feel the wind. It’s just so incredible. I’ve never seen the Amazon from that perspective before, even from a plane.

The 1,066-foot-high Amazon Tall Tower Observatory (ATTO) in the Amazon rainforest of Brazil.

Raphael Alves/AFP via Getty Images

At that point in my research, I now understood so much more than I had in the very beginning. And I could see the clouds and the blue sky, the soil and plant life below me, and the rain that was about to come down. This was all made by life or a product of life in some way. I already felt so much reverence for nature and for our living world, but this [reporting process] really amplified that for me because I just hadn’t realized how thoroughly intertwined biology is in the planet’s structure and chemistry and all of its geological processes. What I previously saw as pure geology or pure meteorology, I now see as infused with life. And wherever it is infused with life, it becomes all the more wondrous.

You mentioned reverence, and there’s almost a spirituality to this idea of all living things being one. It seems like that’s part of why Lovelock’s Gaia hypothesis was laughed off by scientists. Why do you think science is now starting to embrace this?

In the decades since, a huge amount of evidence has shown that some of the core tenets of what Lovelock was saying are indeed true. His initial insight was that wherever life emerges, it inevitably transforms its home planet. When Lovelock was working for NASA to help find life on other planets, he realized that you should just look at the atmospheric chemistry of other planets because if there’s life there, it has probably changed that chemistry dramatically. The idea that wherever life emerges, it transforms Earth— that is now universally accepted within science because we have so much more evidence than he had back then. We understand much better how life oxygenated the planet; how life was involved in the water cycle; how life made fire possible; how life was involved in geology and made new mineral species possible; and how soil came from life.

It’s not that the planet simply has a layer of life on it; it’s that life and Earth are continually changing each other through a single process. Claiming that Earth itself is alive remains provocative and controversial, and yet I have found scientists ranging from astrobiologists to paleontologists to atmospheric chemists who completely embrace the notion of a living planet now or are increasingly open to that perspective. These ideas have really come to the forefront now, and I think we’re going see it emphasized a lot in the very near future.

It seems like wherever Earth and life have found an equilibrium, we humans have found a way to tip that out of balance. How have you come to think about our species’ impact on the planet?

I don’t want to make too strong of an analogy between ecosystems and organisms, but sometimes I think of the immune system when I’m thinking about this. The immune system, on a day-to-day basis, does an incredible job of protecting us from threats that we’re not even aware of. But it goes wrong all the time, [such as with] autoimmune disorders and allergies. You change the wrong variable in the right way and it can turn on you.

There’s an analogous thing that happens with the Earth system. If you perturb it in the wrong way or to too great an extent, the whole thing starts to fall apart. But that’s happened so many times throughout Earth’s history, and every single time, Earth has not only recovered but has also arguably become more complex and diverse than it was before. Earth has this astonishing ability to endure these catastrophes, pull back to its fundamentals and then reflourish over time. I take a lot of solace in that.

Unfortunately, as a species and as a civilization, we cannot depend on these super long-term processes [to save us]. The living planet is probably going to be okay regardless of what we do—millions of years from now, it will recover. But we, or our way of life, may not survive if we allow it to go too far. That is exactly why we have to intervene as rapidly as possible.

You made an observation that really struck me in the book—that we are only coming to understand the complexity and interconnectedness of the living Earth system as we’re on the verge of destroying it. How you think about where that leaves us and our responsibility?

I think the Anthropocene has underscored for us what is truly vital and essential in this world. Anything we do as living creatures influences our environment, and then that loops back to influence us directly. In contrast to many nonhuman life-forms, which have gradually co-evolved these rhythms that tend to stabilize the planet, we’ve done the opposite and, in a geological blink, have massively perturbed those rhythms. I think that clarifies for us exactly what our responsibility is, compared with all other life-forms. We may all be part of the system and participating in it, but as far as we know, only we are consciously aware of the system as a whole. We’re the only ones building supercomputers that can model the entire Earth system and debating what to do about climate change.

There’s something empowering about this framework. Whereas most species are stuck to the very slow process of evolutionary change, we humans can consciously choose in the moment to change what we are doing. We have both this privilege and responsibility, not just to each other and to other life-forms but to the larger living system of which we are a part. That’s something we’re seeing with this increasing movement of personhood rights for nature: for ecosystems, for mountains, and for rivers and forests. We have to recognize that this moral responsibility extends to our environments as well, not just to living things and certainly not just to our species.