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Hypothesis Examples

A hypothesis is a prediction of the outcome of a test. It forms the basis for designing an experiment in the scientific method . A good hypothesis is testable, meaning it makes a prediction you can check with observation or experimentation. Here are different hypothesis examples.

Null Hypothesis Examples

The null hypothesis (H 0 ) is also known as the zero-difference or no-difference hypothesis. It predicts that changing one variable ( independent variable ) will have no effect on the variable being measured ( dependent variable ). Here are null hypothesis examples:

• Plant growth is unaffected by temperature.
• If you increase temperature, then solubility of salt will increase.
• Incidence of skin cancer is unrelated to ultraviolet light exposure.
• All brands of light bulb last equally long.
• Cats have no preference for the color of cat food.
• All daisies have the same number of petals.

Sometimes the null hypothesis shows there is a suspected correlation between two variables. For example, if you think plant growth is affected by temperature, you state the null hypothesis: “Plant growth is not affected by temperature.” Why do you do this, rather than say “If you change temperature, plant growth will be affected”? The answer is because it’s easier applying a statistical test that shows, with a high level of confidence, a null hypothesis is correct or incorrect.

Research Hypothesis Examples

A research hypothesis (H 1 ) is a type of hypothesis used to design an experiment. This type of hypothesis is often written as an if-then statement because it’s easy identifying the independent and dependent variables and seeing how one affects the other. If-then statements explore cause and effect. In other cases, the hypothesis shows a correlation between two variables. Here are some research hypothesis examples:

• If you leave the lights on, then it takes longer for people to fall asleep.
• If you refrigerate apples, they last longer before going bad.
• If you keep the curtains closed, then you need less electricity to heat or cool the house (the electric bill is lower).
• If you leave a bucket of water uncovered, then it evaporates more quickly.
• Goldfish lose their color if they are not exposed to light.
• Workers who take vacations are more productive than those who never take time off.

Is It Okay to Disprove a Hypothesis?

Yes! You may even choose to write your hypothesis in such a way that it can be disproved because it’s easier to prove a statement is wrong than to prove it is right. In other cases, if your prediction is incorrect, that doesn’t mean the science is bad. Revising a hypothesis is common. It demonstrates you learned something you did not know before you conducted the experiment.

Test yourself with a Scientific Method Quiz .

• Mellenbergh, G.J. (2008). Chapter 8: Research designs: Testing of research hypotheses. In H.J. Adèr & G.J. Mellenbergh (eds.), Advising on Research Methods: A Consultant’s Companion . Huizen, The Netherlands: Johannes van Kessel Publishing.
• Popper, Karl R. (1959). The Logic of Scientific Discovery . Hutchinson & Co. ISBN 3-1614-8410-X.
• Schick, Theodore; Vaughn, Lewis (2002). How to think about weird things: critical thinking for a New Age . Boston: McGraw-Hill Higher Education. ISBN 0-7674-2048-9.
• Tobi, Hilde; Kampen, Jarl K. (2018). “Research design: the methodology for interdisciplinary research framework”. Quality & Quantity . 52 (3): 1209–1225. doi: 10.1007/s11135-017-0513-8

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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 .

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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

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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.

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Hypotheses and Proofs

In this post

What is a hypothesis?

A hypothesis is basically a theory that somebody states that needs to be tested in order to see if it is true. Most of the time a hypothesis is a statement which someone claims is true and then a series of tests are made to see if the person is correct.

Hypothesis – a proposed true statement that acts as a starting point for further investigation.

Devising theories is how all scientists progress, not just mathematicians, and the evidence that is found must be collected and interpreted to see if it gives any light on the truth in the statement. Statistics can either prove or disprove a theory, which is why we need the evidence that we gather to be as close to the truth as possible: so that we can give an answer to the question with a high level of confidence.

Hypotheses are just the plural of a single hypothesis. A hypothesis is the first thing that someone must come up with when doing a test, as we must initially know what it is we wish to find out rather than blindly going into carrying out certain surveys and tests.

Some examples of hypotheses are shown below:

• Britain is colder than Spain
• A dog is faster than a cat
• Blondes have more fun
• The square of the hypotenuse of a triangle is equal to the sum of the squares of the other two sides

Obviously, some of these hypotheses are correct and others are not. Even though some may look wrong or right we still need to test the hypothesis either way to find out if it is true or false.

Some hypotheses may be easier to test than others, for example it is easy to test the last hypothesis above as this is very mathematical. However, when it comes to measuring something like ‘fun’ which is shown in the hypothesis ‘Blondes have more fun’ we will begin to struggle! How do you measure something like fun and in what units? This is why it is much easier to test certain hypotheses when compared with others.

Another way to come up with a hypothesis is by doing some ‘trial and error’ type testing. When finding data you may realise that there is in fact a pattern and then state this as a hypothesis of your findings. This pattern should then be tested using mathematical skills to test its authenticity. There is still a big difference between finding a pattern in something and finding that something will always happen no matter what. The pattern that is found at any point may just be a coincidence as it is much harder to prove something using mathematics rather than simply noticing a pattern. However, once something is proved with mathematics it is a very strong indication that the hypothesis is not only a guess but is scientific fact.

A hypothesis must always:

• Be a statement that needs to be proven or disproven, never a question
• Be applied to a certain population
• Be testable, otherwise the hypothesis is rather pointless as we can never know any information about it!

There are also two different types of hypothesis which are explained here:

An Experimental Hypothesis –  This is a statement which should state a difference between two things that should be tested. For example, ‘Cheetahs are faster than lions’.

A Null Hypothesis –  This kind of hypothesis does not say something is more than another, instead it states that they are the same. For example, ‘There is no difference between the number of late buses on Tuesday and on Wednesday’.

Subjects and samples

We have already talked in an earlier lesson of different types of samples and how these are formed, so we will not dwell for too long on this. The main thing to make sure of when choosing subjects for a test is to link them to the hypothesis that we are looking into. This will then give a much better data set that will be a lot more relevant to the questions we are asking. There is no point in us gathering data from people that live in Ireland if our original hypothesis states something about Scottish people, so we need to also make sure that the sample taken is as relevant to the hypothesis as possible. As with all samples that are taken, there should never be any bias towards one subject or another (unless we are using something like quota sampling as outlined in an earlier lesson). This will then mean that a random collection of subjects is taken into account and will mean that the information that is acquired will be more useful to the hypothesis that we wish to look at.

The experimental method

By treating the hypothesis and the data collection as an experiment, we should use as many scientific methods as possible to ensure that the data we are collecting is very accurate.

The most important and best way of doing this is the  control of variables . A variable is basically anything that can change in a situation, which means there are a lot in the vast majority as lots of different things can be altered. By keeping all variables the same and only changing the ones which we wish to test, we will get data that is as reliable as possible. However, if variables are changed that can affect an outcome we may end up getting false data.

For example, when testing ‘A cheetah is faster than a lion’ we could simply make the two animals run against each other and see which is quickest. However, if we allowed the cheetah to run on flat ground and made the lion run up hill, then the times would not be accurate to the truth as it is much harder to run up a slope than on flat ground. It is for this reason that any variables should be the same for all subjects.

The only variable that is mentioned in the hypothesis ‘A cheetah runs faster than a lion’ is the animal that runs. Therefore, this is called the  independent variable  and is the only thing that we wish to change between experiments as it is the thing we wish to  prove has an effect on other results.

A  dependent variable  is something that we wish to measure in experiments to see if there is an effect. This is the speed at which something runs in our example, as we are changing the animal and measuring the speed.

Independent variable – something that stands alone and is not changed by other variables in the experiment. This variable is changed by the person carrying out the investigation to see if it influences the dependent variables. This can also be seen as an input when an experiment is created.

Dependent variable – this variable is measured in an experiment to see if it changes when the independent variable is changed. These represent an output after the experiment is carried out.

Standardised instructions

Another thing that is essential to carrying out experiments is to give both of the participants the same instructions in what you wish them to do. Although this may seem a little picky, there will be a definite difference in how a subject performs if they are given clear and concise instructions as opposed to given misleading and rushed ones.

Turning data into information

Experiments are carried out to produce a set of data but this is not the end of the problem! We will then need to interpret and change this information into something that will tell us what we need to know. This means we need to turn data in the form of numbers into actual information that can be useful to our investigation. Figures that are found through experiments are first shown as ‘raw data’ before we can use different tables and charts to show the patterns that have been found in the surveys and experiments that have been carried out. Once all the data is collected and in tables we can move on to using these to find patterns.

Once a hypothesis has been stated, we can look to prove or disprove it. In mathematics, a proof is a little different to what people usually think. A mathematical proof must show that something is the case without any doubt. We do this by working through step-by-step to build a proof that shows the hypothesis as being either right or wrong. Each small step in the proof must be correct so that the entire thing cannot be argued.

Setting out a proof

Being able to write a proof does not mean that you must work any differently to how you would usually answer a question. It simply means that you must show that something is the case. Questions on proofs may ask you to ‘prove’, ‘verify’ or ‘check’ a statement.

When doing this you will need to first understand the hypothesis that has been stated. Look at the example below to see how we would go about writing a simple proof.

Prove that 81 is not a prime number.

Here we have a hypothesis that 81 is not prime. So, to prove this, we can try to find a factor of 81 that is not 1 as we know the definition of a prime number is that it is only divisible by itself and 1. Therefore, we could simply show that:

The fact that 81 divided by 9 gives us 9 proves the hypothesis that 81 is not prime.

A proof for a hypothesis does not have to be very complex – it simply has to show that a statement is either true or false. Doing this will use your problem-solving skills though, as you may need to think outside the box and ensure that all of the information that you have is fully understood.

Harder examples

Being able to prove something can be very challenging. It is true that some mathematical equations are still yet to be proved and many mathematicians work on solving extremely complex proofs every day.

When looking at harder examples of proofs you will need to find like terms in equations and then think about how you can work through the proof to get the desired result.

Here we need to use the left-hand side to get to the right-hand side in order to prove that they are equal. We can do this by expanding the brackets on the left and collecting the like terms:

We have now expanded the brackets and collected the like terms. It is now that we will need to look at our hypothesis again and try to make the above equation into the right-hand side by moving terms around. We can see from the right-hand side of our hypothesis that we have a double bracket and then 2 added to this so we can begin by bringing 2 out of the above:

So we have now worked through an entire proof from start to finish. Here it is again using only mathematics and no writing:

In the above we have shown that the hypothesis is true by working through step-by-step and rearranging the equation on the left to get the one on the right.

The step-by-step approach to proofs

To prove something is correct we have used a step-by-step approach so far. This method is a very good way to get from the left-hand side of an equation to the right-hand side through different steps. To do this we can use specific rules:

1) Try to multiply out brackets early on where possible.  This will help you to cancel out certain terms in order to simplify the equation.

3) Take small steps each time.  A proof is about working through a problem slowly so that it is easy to spot what has been done in each step. Do not take big leaps in your work such as multiplying out brackets and collecting like terms all at once. Remember that the person marking your paper needs to see your working, so it is good to work in small stages.

4) Go back and check your work.  Once you have finished your proof you can go back and check each individual stage. One of the good things about carrying out a proof is that you will know if a mistake has been made in your arithmetic because you will not be able to get to the final solution. If this happens, go back and check your working throughout.

Harder proofs

When working through a proof that is more difficult it can be quite tricky. Sometimes we may have to carry out a lot of different steps or even prove something using another piece of knowledge. For example, it might be that we are asked to prove that an expression will always be even or that it will always be positive.

In the above equation we have worked through to get an answer that is completely multiplied by 4. This must therefore be even as any number (whether even or odd) will be even when multiplied by 4.

In this example we have had to use our knowledge that anything multiplied by 4 must be even. This information was not included in the question but is something that we know from previous lessons. Some examples of information that you may need to know in order to solve more difficult proofs are:

Any number that is multiplied by an even number must be even

A number multiplied by an even number and then added to an odd number will be odd

Any number multiplied by a number will give an answer that is divisible by the same number (e.g. 3 n  must be divisible by 3)

Any number that is squared must be positive

Above we have come to an answer that is multiplied by 3. This means that the answer has to be divisible by 3 also.

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Research Hypothesis In Psychology: Types, & Examples

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

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

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

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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

On This Page:

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

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

Some key points about hypotheses:

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

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

Types of Research Hypotheses

Alternative hypothesis.

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

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

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

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

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

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

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

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

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

Null Hypothesis

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

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

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

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

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

Nondirectional Hypothesis

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

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

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

Directional Hypothesis

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

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

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

Falsifiability

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

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

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

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

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

Can a Hypothesis be Proven?

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

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

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

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

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

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

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

How to Write a Hypothesis

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

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

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

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

More Examples

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

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• 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.

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Writing a Hypothesis & Prediction

A prediction and a hypothesis are different. However, experiments should include both a hypothesis and a prediction.

• A hypothesis is normally generated from an idea or observation.

Examples of hypotheses

• Adding water to a sunflower will help it grow.
• An increase in temperature will increase the rate of reaction.
• A change in pH will affect how an enzyme works.

• The prediction will explain how your hypothesis can be tested.
• The prediction states a relationship between two variables.
• The stated relationship should be suggested in the hypothesis.

Examples of predictions

• If I increase the amount of water I use to water the plant, it will grow more.
• If I decrease the temperature, the rate of reaction will decrease.
• If I increase the pH, the rate of activity will increase.

The word 'because'

• Once you have written the prediction, you can extend your work by using the word ‘because’.
• Use your scientific knowledge to explain your prediction.

1.1 Cells, Tissues & Organs

1.1.1 Microscopes

1.1.2 Magnification

1.1.3 Multicellular Organisms

1.1.4 Tissues

1.1.5 Organs

1.1.6 Unicellular Organisms

1.1.7 Diffusion

1.1.8 Factors Affecting Diffusion

1.1.9 Plant Cells

1.1.10 Cellulose

1.1.11 Plant Tissues

1.1.12 Leaves

1.1.13 Animal Cells

1.1.14 Comparing Animal & Plant Cells

1.1.15 How to Make a Model Animal and Plant Cell

1.1.16 Specialised Cells

1.1.17 Stem Cells

1.1.18 Uses of Stem Cells

1.1.19 Disadvantages of Stem Cells

1.1.20 Blood Components

1.1.21 Platelets

1.1.22 End of Topic Test - Cells & Organisation

1.1.23 The Lungs

1.1.24 Breathing

1.1.25 Plant Gas Exchange

1.1.26 Health

1.1.27 End of Topic Test - Living Organisms

1.2 Reproduction & Variation

1.2.1 Reproduction in Humans

1.2.2 Male Reproductive System

1.2.3 Female Reproductive System

1.2.4 Gestation

1.2.5 Pregnancy

1.2.6 Puberty

1.2.7 The Menstrual Cycle

1.2.8 Reproduction in Plants

1.2.9 Pollination

1.2.10 Dispersal Method

1.2.11 Variation

1.2.12 Causes of Variation

1.2.13 Inheritance

1.2.14 Adaptations and Evolution

1.2.15 Species & Selective Breeding

1.2.16 Genetic Conditions

1.2.17 End of Topic Test - Reproduction & Variation

1.3 Ecological Relationships & Classification

1.3.1 Species Interdependence

1.3.2 Food Chains & Webs

1.3.3 Changes to Food Webs 1

1.3.4 Changes to Food Webs 2

1.3.5 Relationships in an Ecosystem

1.3.6 The Impact of Environmental Change

1.3.7 Decomposers

1.3.8 Decay

1.3.9 Assessing Ecosystems

1.3.10 Ecological Sampling

1.3.11 Required Practical - Estimating Population Size

1.3.12 Pyramids of Number and Biomass

1.3.13 Classification of Living Organisms

1.3.14 Competition Between Organisms

1.3.15 Adaptations of Plants

1.3.16 Natural Selection

1.3.17 Evidence for Evolution

1.3.18 Environmental Changes & Extinctions

1.3.19 The Importance of Biodiversity

1.3.20 Bioaccumulation

1.3.21 End of Topic Test - Material Cycles & Energy

1.4 Digestion & Nutrition

1.4.1 Balanced Diets

1.4.2 Vitamins & Minerals

1.4.3 Protein

1.4.4 Lipids, Oils and Fats

1.4.5 Carbohydrates

1.4.6 Starch

1.4.7 Energy Needs

1.4.8 Dietary Fibre

1.4.9 Diseases Caused by Nutritional Deficiencies

1.4.10 Digestion

1.4.11 Plant Nutrition

1.4.12 Enzymes in Digestion

1.4.13 Required Practical - Enzymes in Digestion

1.5 Plants & Photosynthesis

1.5.1 Roots

1.5.2 Photosynthesis

1.5.3 Leaves

1.5.4 Rate of Photosynthesis

1.5.5 Testing the Rate of Photosynthesis

1.5.6 Water Transport in Plants

1.5.7 Translocation

1.5.8 The Carbon Cycle

1.5.9 Human Activities & Carbon Dioxide

1.6 Biological Systems & Processes

1.6.1 Living Organisms

1.6.2 Dichotomous Keys

1.6.3 Biomechanics

1.6.4 Muscles

1.6.5 The Skeleton

1.6.6 Measuring Forces

1.6.7 Antagonistic Muscle Pairings

1.6.8 The Respiratory System

1.6.9 Structure & Function of the Gas Exchange System

1.6.10 Breathing

1.6.11 Respiration

1.6.12 Respiration During Exercise

1.6.13 Anaerobic Respiration

1.6.14 Lactic Acid

1.6.15 Effects of Smoking on the Respiratory System

1.6.16 Balanced Diets

1.6.17 Human Growth & Development

1.6.19 Alleles

1.6.20 Genotype vs Phenotype

1.6.21 Punnett Squares

1.6.22 Joints

1.6.23 The Renal System

1.6.24 The Double Circulatory System

1.6.25 Heart and Blood

1.6.26 Blood Vessels

1.6.27 Glucose

1.6.28 Glucose and Diabetes

1.6.29 The Effects of Recreational Drug Use

1.6.30 Human Illnesses

1.6.31 Antibiotics

1.6.32 Vaccinations

1.6.33 How Antibiotics and Vaccines Work

1.6.34 Mental Health

2 Chemistry

2.1 Particles

2.1.1 Particles

2.1.2 States of Matter

2.1.3 Changes of State

2.1.4 Properties of States of Matter

2.1.5 Diffusion

2.1.6 Changing State

2.1.7 Pressure

2.1.8 Temperature Increase in a Gas

2.1.9 Conservation of Mass

2.1.10 Purity of Substances

2.1.11 Pure Substances

2.1.12 Evaporation

2.1.13 Mixtures

2.1.14 Separating Mixtures

2.1.15 Distillation

2.1.16 Chromatography

2.1.17 Solubility

2.1.18 Investigating Solubility

2.2 Chemical Reactions

2.2.1 Chemical Reactions

2.2.2 Common Reactions

2.2.3 Acids & Alkalis

2.2.4 Reactions of Acids

2.2.5 Testing for Hydrogen

2.2.6 The pH Scale

2.2.7 Titration

2.2.8 End of Topic Test - Chemical Reactions

2.3 Atoms, Elements, Compounds

2.3.1 Atoms

2.3.2 Elements

2.3.3 Compounds & Mixtures

2.3.4 Electron Configuration

2.3.5 Chemical Symbols

2.3.6 Chemical Formulae

2.3.7 Conservation of Mass

2.3.8 Vacuums

2.3.9 Molecules

2.3.10 End of Topic Test - Particles & Atoms

2.4 The Periodic Table

2.4.1 Physical Properties

2.4.2 Chemical Properties

2.4.3 The Periodic Table

2.4.4 Metals

2.4.5 Non-Metals

2.4.6 Alkali Metals

2.4.7 Halogens

2.4.8 Oxides

2.4.9 End of Topic Test - The Periodic Table

2.5 Materials & the Earth

2.5.1 The Composition of The Earth

2.5.2 The Structure of the Earth

2.5.3 Igneous Rocks

2.5.4 Sedimentary Rocks

2.5.5 Metamorphic Rocks

2.5.6 The Rock Cycle

2.5.7 Physical Weathering

2.5.8 Chemical Weathering

2.5.9 Biological Weathering

2.5.10 The Formation of Fossils

2.5.11 Crude Oil

2.5.12 End of Topic Test - Earth

2.5.13 The Earth's Early Atmosphere

2.5.14 The Earth's Atmosphere Today

2.5.15 Oxygen in the Atmosphere

2.5.16 Carbon Dioxide in the Atmosphere

2.5.17 Greenhouse Gases

2.5.18 Climate Change

2.5.19 Resources

2.5.20 Recycling

2.5.21 Ceramics

2.5.22 Polymers

2.5.23 Composites

2.5.24 End of Topic Test - Materials

2.5.25 End of Topic Test - Polymers

2.6 Reactivity

2.6.2 Ionic Bonding

2.6.3 State Symbols

2.6.4 Balancing Chemical Equations

2.6.5 Relative Formula Mass

2.6.6 Calculating the Relative Formula Mass

2.6.7 The Reactivity Series

2.6.8 Carbon & The Reactivity Series

2.6.9 Displacement Reactions

2.6.10 Displacement Reactions - Halogens

2.6.11 Alloys

2.6.12 Metal Alloys

2.7 Energetics

2.7.1 Measuring Gas Production

2.7.2 Observing a Colour Change

2.7.3 Analysing Reaction Rates

2.7.4 Factors Affecting the Rate of Reaction

2.7.5 Catalysts

2.7.6 Testing for Oxygen

2.7.7 Energy Changes During Reactions

2.8 Properties of Materials

2.8.1 Testing for Gases

2.8.2 Alloys

2.8.3 Density

2.8.4 Density of Solids, Liquids & Gases HyperLearning

3.1.1 Energy Stores & Pathways

3.1.2 Energy Transfers

3.1.3 Common Energy Transfers

3.1.4 Wasted Energy

3.1.5 Efficiency of Energy Transfer

3.1.6 Sankey Diagrams

3.1.7 Heat & Temperature

3.1.8 Heat Transfer

3.1.9 Conductors vs Insulators

3.1.10 Reducing Energy Transfers

3.1.11 Energy & Power

3.1.12 Energy in Food

3.1.13 Calories

3.1.14 Food Labels

3.1.15 Energy at Home

3.1.16 Fuel Bills

3.1.17 Calculating Fuel Bills

3.1.18 Non-Renewable Energy - Fossil Fuels

3.1.19 Other Non-Renewables

3.1.20 Renewable Energy - Air & Ground

3.1.21 Renewable Energy - Water

3.1.22 End of Topic Test - Energy

3.2 Forces & Motion

3.2.1 Forces

3.2.2 Contact Forces

3.2.3 Balanced Forces

3.2.4 Force Diagrams & Resultant Forces

3.2.5 Free Body Diagram - Uses

3.2.6 Force & Acceleration

3.2.7 Gravity

3.2.8 Weight

3.2.9 Pressure

3.2.10 Speed

3.2.11 Relative Motion

3.2.12 Friction

3.2.13 Water & Air Resistance

3.2.14 Distance-Time Graphs

3.2.15 Moments

3.2.16 Levers

3.2.17 Work

3.2.18 Machines

3.2.19 Work & Machines

3.2.20 Elasticity

3.2.21 Elasticity - Hooke's Law

3.2.22 Density

3.2.23 Floating & Sinking

3.2.24 End of Topic Test - Forces & Motion

3.2.25 Vacuums

3.2.26 Thermal Energy & Conduction

3.2.27 Convection & Radiation

3.2.28 Evaporation

3.3.1 Waves

3.3.2 Types of Waves

3.3.3 Observing Waves

3.3.4 Wave Speed

3.3.5 Earthquakes

3.3.6 Sound Waves

3.3.7 Uses of Sound Waves

3.3.8 The Interactions of Sound with Different Mediums

3.3.9 Reflecting Sounds

3.3.10 The Speed of Sound

3.3.11 Measuring the Speed of Sound

3.3.12 The Hearing Range of Humans

3.3.13 The Human Ear

3.3.14 Light Waves

3.3.15 Reflection

3.3.16 Drawing a Reflected Image

3.3.17 Refraction

3.3.18 The Human Eye

3.3.19 The Eye as a Pinhole Camera

3.3.20 Lenses

3.3.21 Colour

3.3.22 Seeing Colour

3.3.23 Colours of Light

3.3.24 Drawing Waves

3.3.25 Wave Interactions

3.3.26 Comparing Sound & Light

3.3.27 End of Topic Test - Waves

3.3.28 End of Topic Test - Sound

3.4 Electricity & Magnetism

3.4.1 Circuit Symbols

3.4.2 Resistors & Diodes

3.4.3 Electric Current

3.4.4 Measuring Current

3.4.5 Potential Difference

3.4.6 Series Circuits

3.4.7 Parallel Circuits

3.4.8 Resistance

3.4.9 Charges

3.4.10 Static Electricity

3.4.11 Magnets

3.4.12 Magnetic Fields

3.4.13 The Earth's Field

3.4.14 Electromagnetism

3.4.15 Uses of Electromagnets

3.4.16 Strength of Magnetic Fields

3.4.17 Circuit Symbols HyperLearning

3.5.1 Physical Reactions

3.5.2 Changes of State

3.5.3 Particles

3.5.4 Density

3.5.5 Density & the Particle Model

3.5.6 The Equation for Density

3.5.7 Dissolving

3.5.8 Brownian Motion

3.5.9 Diffusion

3.5.10 Filtration

3.5.11 Solids

3.5.12 Liquids

3.5.13 Gases

3.5.14 Weight & Mass

3.5.15 Gravity

3.5.16 Gravitational Field Strength

3.5.17 Gravity in Space

3.5.18 Atmospheric Pressure

3.5.19 Liquid Pressure

3.5.20 End of Topic Test - Matter

3.6 Space Physics

3.6.1 The Sun

3.6.2 The Planets

3.6.3 Other Astronomical Bodies

3.6.4 The Milky Way

3.6.5 Beyond The Milky Way

3.6.6 The Seasons

3.6.7 Days, Months & Years

3.6.8 The Moon

3.6.9 Light Years

3.6.10 End of Topic Test - Space

4 Thinking Scientifically

4.1 Models & Representations

4.1.1 Strengths & Limitations of Models

4.1.2 Symbols & Formulae to Represent Scientific Ideas

4.1.3 Analogies in Science

4.1.4 Changing Models – Atomic Theory

4.1.5 Working Safely in the Lab

4.1.6 Variables

4.1.7 Writing a Hypothesis & Prediction

4.1.8 Planning an Experiment

4.1.9 Maths Skills for Science

4.1.10 Drawing Scientific Apparatus

4.1.11 Observation & Measurement Skills

4.1.12 Types of Data

4.1.13 Graphs & Charts

4.1.14 Bias in Science

4.1.15 Conclude & Evaluate

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Planning an Experiment

Scientific Hypothesis Examples

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• B.A., Physics and Mathematics, Hastings College

A hypothesis is an educated guess about what you think will happen in a scientific experiment, based on your observations. Before conducting the experiment, you propose a hypothesis so that you can determine if your prediction is supported.

There are several ways you can state a hypothesis, but the best hypotheses are ones you can test and easily refute. Why would you want to disprove or discard your own hypothesis? Well, it is the easiest way to demonstrate that two factors are related. Here are some good scientific hypothesis examples:

• Hypothesis: All forks have three tines. This would be disproven if you find any fork with a different number of tines.
• Hypothesis: There is no relationship between smoking and lung cancer. While it is difficult to establish cause and effect in health issues, you can apply statistics to data to discredit or support this hypothesis.
• Hypothesis: Plants require liquid water to survive. This would be disproven if you find a plant that doesn't need it.
• Hypothesis: Cats do not show a paw preference (equivalent to being right- or left-handed). You could gather data around the number of times cats bat at a toy with either paw and analyze the data to determine whether cats, on the whole, favor one paw over the other. Be careful here, because individual cats, like people, might (or might not) express a preference. A large sample size would be helpful.
• Hypothesis: If plants are watered with a 10% detergent solution, their growth will be negatively affected. Some people prefer to state a hypothesis in an "If, then" format. An alternate hypothesis might be: Plant growth will be unaffected by water with a 10% detergent solution.
• What Are Examples of a Hypothesis?
• What Is a Hypothesis? (Science)
• What Is a Testable Hypothesis?
• Null Hypothesis Examples
• What Are the Elements of a Good Hypothesis?
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• What Is the Difference Between a Control Variable and Control Group?
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• What Is a Controlled Experiment?

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How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis.

• Operationalization

Hypothesis Types

Hypotheses examples.

• Collecting Data

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

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

At a Glance

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

The Hypothesis in the Scientific Method

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

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

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

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

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

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

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

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

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

Elements of a Good Hypothesis

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

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

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

How to Formulate a Good Hypothesis

To form a hypothesis, you should take these steps:

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

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

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

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

The Importance of Operational Definitions

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

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

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

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

Replicability

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

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

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

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

Hypothesis Checklist

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

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

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

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

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

A few examples of simple hypotheses:

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

Examples of a complex hypothesis include:

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

Examples of a null hypothesis include:

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

Examples of an alternative hypothesis:

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

Collecting Data on Your Hypothesis

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

Descriptive Research Methods

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

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

Experimental Research Methods

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

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

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

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

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

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

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

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

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

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

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

What is a Hypothesis – Types, Examples and Writing Guide

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Definition:

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

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

Types of Hypothesis

Types of Hypothesis are as follows:

Research Hypothesis

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

Null Hypothesis

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

Alternative Hypothesis

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

Directional Hypothesis

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

Non-directional Hypothesis

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

Statistical Hypothesis

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

Composite Hypothesis

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

Empirical Hypothesis

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

Simple Hypothesis

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

Complex Hypothesis

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

Applications of Hypothesis

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

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

How to write a Hypothesis

Here are the steps to follow when writing a hypothesis:

Identify the Research Question

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

Conduct a Literature Review

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

Determine the Variables

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

Formulate the Hypothesis

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

Write the Null Hypothesis

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

Refine the Hypothesis

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

Examples of Hypothesis

Here are a few examples of hypotheses in different fields:

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

Purpose of Hypothesis

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

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

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

When to use Hypothesis

Here are some common situations in which hypotheses are used:

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

Characteristics of Hypothesis

Here are some common characteristics of a hypothesis:

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

Advantages of Hypothesis

Hypotheses have several advantages in scientific research and experimentation:

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

Limitations of Hypothesis

Some Limitations of the Hypothesis are as follows:

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

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Published 2008 Revised 2019

Understanding Hypotheses

'What happens if ... ?' to ' This will happen if'

The experimentation of children continually moves on to the exploration of new ideas and the refinement of their world view of previously understood situations. This description of the playtime patterns of young children very nicely models the concept of 'making and testing hypotheses'. It follows this pattern:

• Make some observations. Collect some data based on the observations.
• Draw a conclusion (called a 'hypothesis') which will explain the pattern of the observations.
• Test out your hypothesis by making some more targeted observations.

So, we have

• A hypothesis is a statement or idea which gives an explanation to a series of observations.

Sometimes, following observation, a hypothesis will clearly need to be refined or rejected. This happens if a single contradictory observation occurs. For example, suppose that a child is trying to understand the concept of a dog. He reads about several dogs in children's books and sees that they are always friendly and fun. He makes the natural hypothesis in his mind that dogs are friendly and fun . He then meets his first real dog: his neighbour's puppy who is great fun to play with. This reinforces his hypothesis. His cousin's dog is also very friendly and great fun. He meets some of his friends' dogs on various walks to playgroup. They are also friendly and fun. He is now confident that his hypothesis is sound. Suddenly, one day, he sees a dog, tries to stroke it and is bitten. This experience contradicts his hypothesis. He will need to amend the hypothesis. We see that

• Gathering more evidence/data can strengthen a hypothesis if it is in agreement with the hypothesis.
• If the data contradicts the hypothesis then the hypothesis must be rejected or amended to take into account the contradictory situation.

• A contradictory observation can cause us to know for certain that a hypothesis is incorrect.
• Accumulation of supporting experimental evidence will strengthen a hypothesis but will never let us know for certain that the hypothesis is true.

In short, it is possible to show that a hypothesis is false, but impossible to prove that it is true!

Whilst we can never prove a scientific hypothesis to be true, there will be a certain stage at which we decide that there is sufficient supporting experimental data for us to accept the hypothesis. The point at which we make the choice to accept a hypothesis depends on many factors. In practice, the key issues are

• What are the implications of mistakenly accepting a hypothesis which is false?
• What are the cost / time implications of gathering more data?
• What are the implications of not accepting in a timely fashion a true hypothesis?

For example, suppose that a drug company is testing a new cancer drug. They hypothesise that the drug is safe with no side effects. If they are mistaken in this belief and release the drug then the results could have a disastrous effect on public health. However, running extended clinical trials might be very costly and time consuming. Furthermore, a delay in accepting the hypothesis and releasing the drug might also have a negative effect on the health of many people.

In short, whilst we can never achieve absolute certainty with the testing of hypotheses, in order to make progress in science or industry decisions need to be made. There is a fine balance to be made between action and inaction.

Hypotheses and mathematics So where does mathematics enter into this picture? In many ways, both obvious and subtle:

• A good hypothesis needs to be clear, precisely stated and testable in some way. Creation of these clear hypotheses requires clear general mathematical thinking.
• The data from experiments must be carefully analysed in relation to the original hypothesis. This requires the data to be structured, operated upon, prepared and displayed in appropriate ways. The levels of this process can range from simple to exceedingly complex.

Very often, the situation under analysis will appear to be complicated and unclear. Part of the mathematics of the task will be to impose a clear structure on the problem. The clarity of thought required will actively be developed through more abstract mathematical study. Those without sufficient general mathematical skill will be unable to perform an appropriate logical analysis.

Using deductive reasoning in hypothesis testing

There is often confusion between the ideas surrounding proof, which is mathematics, and making and testing an experimental hypothesis, which is science. The difference is rather simple:

• Mathematics is based on deductive reasoning : a proof is a logical deduction from a set of clear inputs.
• Science is based on inductive reasoning : hypotheses are strengthened or rejected based on an accumulation of experimental evidence.

Of course, to be good at science, you need to be good at deductive reasoning, although experts at deductive reasoning need not be mathematicians. Detectives, such as Sherlock Holmes and Hercule Poirot, are such experts: they collect evidence from a crime scene and then draw logical conclusions from the evidence to support the hypothesis that, for example, Person M. committed the crime. They use this evidence to create sufficiently compelling deductions to support their hypotheses beyond reasonable doubt . The key word here is 'reasonable'. There is always the possibility of creating an exceedingly outlandish scenario to explain away any hypothesis of a detective or prosecution lawyer, but judges and juries in courts eventually make the decision that the probability of such eventualities are 'small' and the chance of the hypothesis being correct 'high'.

• If a set of data is normally distributed with mean 0 and standard deviation 0.5 then there is a 97.7% certainty that a measurement will not exceed 1.0.
• If the mean of a sample of data is 12, how confident can we be that the true mean of the population lies between 11 and 13?

It is at this point that making and testing hypotheses becomes a true branch of mathematics. This mathematics is difficult, but fascinating and highly relevant in the information-rich world of today.

To read more about the technical side of hypothesis testing, take a look at What is a Hypothesis Test?

You might also enjoy reading the articles on statistics on the Understanding Uncertainty website

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Formulation of Testable Hypotheses

For the formulation of testable hypotheses, the psychology specification states you need to know following:

• Null hypothesis and alternative hypothesis.

A hypothesis is simply a formal and testable statement of the relationship between two variables that is to be tested through experimentation. In psychology, as well as other sciences, we use them as part of the scientific method.

The hypothesis is not strictly speaking a prediction and should not be used in the future tense i.e. “this will happen”. It is only at the end of the study that the researcher decides whether the research evidence supports the hypothesis or not.

There are different types of hypotheses used in psychology, however, the main ones that crop up frequently are:

• Directional hypotheses
• Non-directional hypotheses
• Null hypotheses
• Alternative hypotheses

For GCSE Psychology and the AQA specification, we need to know about null hypotheses and alternative hypotheses .

What is a Null Hypothesis?

A null hypothesis is a general statement that the observed variables will have no impact as there is no relationship between them. This hypothesis assumes that any difference observed is due to sampling or experimentation errors.

An example of a null hypothesis for a hypothetical scenario is “watching television before bed has no impact on how well you sleep”

What is an Alternative Hypothesis?

The alternative hypothesis would be a prediction that one variable will affect the other.

An example would be “watching scary movies before bed affects how fast you fall asleep”. The alternative hypothesis does not specify the direction of the outcome, merely that there will be an effect.

Formulating Hypotheses

Once you know enough about hypotheses, you need to consider how to apply them. When conducting research, most of the time the experiment comes from a simple or vague idea we wish to test.

Here’s an example: does music affect peoples ability to learn?

This is rather a vague question and to turn it into a testable experiment, we need to be able to operationalise the two key variables; music and learning .

These two variables are then known as the independent variable and dependent variable – often referred to as the IV and DV for short. More information is given on them below.

Hypotheses are then easier to form, a suitable one for this experiment would be an alternative hypothesis such as:

• “ The presence or absence of music has an effect on the score in a learning test ”

A null hypothesis for this example would simply be:

• “The presence of music has no effect on the score in a learning test”

Type of Variables

For the different types of variables, the GCSE psychology specification states you need to know the following:

• Independent variable, dependent variable, extraneous variables.

There are 3 different types of variables we need to know about which are:

• The independent variable (IV)
• The dependent variable (DV)
• Extraneous variables.

Independent Variable

An experiment will look to measure the effect of one variable on another. These two variables have special names, which are the independent variable and dependent variable.

The independent variable is what researchers manipulate in order to test its effect on the dependent variable (the outcome). Let’s use the example mentioned earlier about music and learning to illustrate this: We are conducting an experiment to see if music affects the ability of students to learn. In this case, the independent variable (IV) we will be manipulating is music.

Within the context of an experiment, we may simply have two conditions where one group is exposed to music while another group is not while engaging in some learning activity. We would then compare the findings to assess the results.

Dependent Variable

The dependent variable (DV) is the outcome or effect we are measuring within the study. So using the example above, the dependent variable would be how well the students are able to learn with or without music. This may be measured in a number of ways (taking a memory test for example or quiz).

So to clarify – the independent variable is what we change and the dependent variable is the outcome we then measure .

A good way to remember the difference is to think of it like this:

• The dependent variable “depends” on what's being changed (the independent variable).
• Another way would be to remember that “we measure the effect of the IV on the DV”.

If you remember that the independent variable (IV) always comes first, you should be able to recall that the dependent variable (DV) is then the outcome. These are just two simple ways of remembering the difference between the IV and DV but feel free to use what works for you.

Extraneous Variable

The extraneous variable is a third variable that may unknowingly be affecting the outcome of the study (the DV).

We conduct experiments to measure the effect of the IV on the DV but sometimes extraneous variables are actually the cause of the changes. They can be seen as “nuisance variables” that affect the study and make it difficult to know whether it is the IV that affects the DV.

Let’s use that example mentioned earlier about how music may affect a students ability to learn. We may conduct this experiment and find that music improves learning as the students who listened to the music performed better.

We may, therefore, conclude music improves students ability to learn, however, what if it was actually a third variable affecting the results which is unaccounted for? (an extraneous variable).

Perhaps we find that the students who performed the best were those with prior knowledge of the questions in the test?. The extraneous variable could then be argued to be prior knowledge participants had that we have not accounted for or could control.

Looking into the study we could perhaps argue the extraneous variable may be the intelligence of participants from one group to another that is affecting the outcome. It may be that some participants in one group were more educated and therefore better problem solvers, and this is an extraneous variable that is affecting the dependent variable (outcome).

With research studies you will be presented, you can almost always find arguments to highlight extraneous variables in some form. It is handy to get into the habit of recognising these different forms as they prove useful in critically analysing studies and topping up your points with further evaluation marks, especially if you go on to study A-level psychology.

Sampling Methods

• Random sampling
• Opportunity sampling
• Systematic sampling
• Stratified sampling
• Strengths and weaknesses of each sampling method
• Understand principles of sampling as applied to scientific data.

This section of AQA GCSE psychology requires you to know about 4 different sampling methods and their strengths and weaknesses.

Sampling methods are merely the different strategies researchers use to get participants for their studies. In any psychological research study, there is usually a target population, which is the group of individuals the researcher is interested in. The aim of the researcher is to try and take a representative sample from this target population using a sampling method. The goal is to gain a representative sample that then allows the researcher to make generalisations across the whole population, based on the findings of this sample.

The four sampling methods you are required to know about are:

Random Sampling

Random sampling involves the researcher identifying members of the target population, numbering them and then attempting to draw out the required number of people for their study.

The selection of participants can be done in a randomised way such as drawing out numbers from a hat if the sample size is small or having a computer randomly select the participants if the sample size is large.

Strengths and Weaknesses of Random Sampling

• Random sampling has the benefit of being more unbiased as all members of the target population have an equal chance of being selected for the study. This would mean that the sample is likely to be more representative of the target population making more valid generalisations possible from the research findings.
• Random sampling also means there is less chance that researchers can influence the results as they have no say as to who is picked. This reduces the impact of investigator effects which means the findings may have more validity.
• However, even despite this, it is still possible for the researcher to end up with an unbalanced and biased sample by chance, particularly if the sample size is too small.
• Gathering randomised samples can also be time-consuming, as attempting to gather enough willing participants from the target population takes a considerable amount of time and effort.

Opportunity Sampling

Opportunity sampling is a form of sampling method that means you ask those who are around you and most easily available , that represent the target population, to participate in the study. This may involve asking those around you in your class, school or people walking in the street for their involvement.

Strengths and Weaknesses of Opportunity Sampling

• The main benefit of opportunity sampling is it is one of the fastest and easiest ways to gather participants for a study when compared to other sampling methods.
• Opportunity samples have a greater chance of being biased because the sample is drawn from a very narrow part of the target population. For example, if you selected participants at school, your sample is likely to consist of mostly students and the behaviours they display in the study may not generalise to adults. Participants may also try to “help” the researcher in a way that would support the hypothesis so the results may be unreliable and invalid.
• With opportunity sampling methods, it is possible the researcher can influence those selected as the process is not randomised. The researcher may select the people they think will support their hypothesis, so investigator effects is a potential hindrance.

Systematic Sampling

Systematic sampling involves selecting every “nth” member of the target population . An example of this would be if the researcher decided that “n” will be “5”, every 5th person in the target population is selected as a participant.

This is still unbiased as the researcher has no influence as to who is picked and it is technically not a “random sample” either as not everyone gets an equal opportunity to be selected (it is only the person 5 positions away). Be sure not to confuse this with the random sampling method due to this slight difference; just remember that there is a fixed systematic way for selection that determines this to be a systematic sample.

Strengths and weaknesses of systematic sampling

• A strength of the systematic sampling method is that it is a simple way for researchers to gather participants and there is little risk of research bias influencing this. Therefore the participants gathered should, in theory, be representative and unbiased which should lead to more reliable results.
• A weakness, however, is participants gathered could still be unrepresentative and biased due to chance selection. This would make the results unreliable when re-tested.
• Another weakness of systematic sampling is you need a bigger sample size to be able to filter out participants based on the “nth” selection. If you require 100 participants for a study and picked them based on every 10 participants, you would need 1000 participants to filter through. Therefore gathering participants for a study based on systematic sampling methods can be very time-consuming.

Stratified Sampling

Stratified sampling is the most complex of the sampling methods and it is most often used in questionnaires. Sub-groups (or strata) within the population are identified (e.g. boys and girls or age groups: 10-12 years, 13-15 years etc) and then participants are gathered from each strata in proportion to their occurrence in the population . The selection of participants is generally done using a random technique.

For example, in a school, there are several subgroups such as teachers, support staff, students and other staff. If the teachers made up 10% of the whole school’s population, then 10% of the sample must be teachers. This is then repeated for each sub-group.

Strengths and Weaknesses of Stratified Sampling

• A major strength of using stratified sampling techniques is that they are very representative of the target population. This means the findings should have high reliability and validity to make generalisations to the target population.
• A major weakness of using stratified sampling is that it is very time-consuming to identify the subgroups, select necessary participants and attempt to get a proportionate sample involved in the study. Therefore this form of sampling method is extremely difficult to execute and can be impractical.

Volunteer Sampling

A volunteer sample consists of people that have volunteered to take part in the study . Volunteers can be gathered in a number of ways such as putting an advert out on the newspaper, internet or some media outlet to try and gather people to take part.

Volunteers may put themselves forward to be part of the study but they may not necessarily be told the aim of the study or what they are really being tested in. For example, Milgram’s shock study gathered volunteers who agreed to take part but did not necessarily know what they were being tested on (obedience).

Strengths and Weaknesses of Volunteer Sampling

• A strength of using volunteer sampling is participants should be willing to give their informed consent to be a part of the study. The people that tend to volunteer tend to be those motivated to take part in the study.
• Volunteer sampling can also be a fast and efficient way of gathering research participants. Instead of having to search for volunteers, an advert could be placed to gather participants based on the traits/characteristics the researcher requires.
• A weakness of using volunteer sampling is the people that tend to volunteer may be a biased sample that are not representative of the target population. For example, volunteers are already motivated to engage in the research (volunteer bias) and more motivated than those that do not and this can influence the outcome of the study in some way.

Designing Research

This section on designing research for GCSE psychology and research methods is quite extensive and requires you to know about quite a few different aspects of designing psychological research studies.

The topics you need to know for research methods include:

Independent group design

• Repeated measures design
• Matched pairs design
• Strengths and weaknesses of each design
• Laboratory experiments
• Field and natural experiments

Questionnaires

• Case studies
• Observation studies
• Strengths and weaknesses of each research method and types of behaviour for which they are suitable.

An independent group design is the simplest to understand and conducted with participants involved in the study usually divided into two subgroups .

One group will take part in the experimental condition (with the independent variable introduced), while the other group would not be exposed to this and form the control group for comparison.

Let’s use the example we mentioned earlier with a study that measures the effects of music on learning.

In an independent group design, one group of participants would be measured on their ability to learn with music being played while the other group would be tested on their learning ability without music.

The results (dependent variable) are then compared between the two groups to measure the effects.

If the results are significantly different then researchers may conclude that this is because of the independent variable, which in our case would be music affecting learning ability.

Strengths and Weaknesses of Independent Group Design

• A strength of using independent group designs is there are no order effects that can invalidate the results, as participants only take part in one of the conditions. Order effects are apparent in experiments where repeated measure designs are used and this involves participants learning or improving from their experience of having to do the experiment more than once. This does not happen in independent group designs which can give more valid results.
• Independent group designs are beneficial as the materials or apparatus can usually be used across both the experimental condition and the control group (minus the independent variable being manipulated or introduced as required). This makes setting up independent group designs far easier than other experimental conditions due to saving time.
• Another strength of independent group designs is that participants are less likely to display demand characteristics. Demand characteristics are when participants change their own behaviour as they figure out (or think they do) the purpose of the study. The participants may then display behaviour that is different in response which can invalidate findings. Demand characteristics are less likely in independent group designs as participants are only exposed to one condition and they don’t have the opportunity to learn or adjust their behaviour in another condition (as they cannot compare).
• A weakness of independent group designs is that differences between the experimental condition and control group may be due to participant variables, such as individual differences between the two groups, rather than the independent variable. Just by probability or chance, one group may be smarter than another or have individual characteristics that make them more able (or less able) for the condition they are exposed. This would then be a confounding variable that affects the results. Using the music example mentioned previously, the group that performs best (whether its the group exposed to music or not) may do so simply because they have more educated or intelligent people than the other condition.
• Another criticism of using independent group designs in experiments is that you need to gather more participants. For example, you need a large enough sample to be exposed to the experimental condition to make generalisations but you then need to gather this number again for the control group condition. Using our example earlier, if we wanted to test how music affects people’s ability to learn and we gather 50 people, we need another 50 people for the control condition that is exposed to no music. Gathering too few participants increases the risks of individual differences being the difference in results while gathering a large number requires more time, effort and resources.

Repeated Measures Design

A repeated measures design sees all the gathered participants of the study being exposed to both conditions of the experiment.

Referring to our music and learning scenario (once again!), we would have a group of 50 participants that would first be exposed to the experimental condition whereby they attempt to learn with music present and then they would attempt to learn without music.

The results would then be compared between the conditions to assess what impact the IV had on the DV. In experiments where there were numerous different conditions, the same participants would be used across them while exposed to different independent variables.

Strengths and Weaknesses of Repeated Measures Design

• A major strength of repeated measure designs is that they require less effort to gather participants as they use the same people across the different experimental conditions. Therefore setting up the experiment tends to be faster compared to group designs such as independent measures where you would require double the amount of participants to cross-compare against.
• Another strength of using repeated measure designs is participant variables are eliminated. This is because the same people are used across the different conditions and they are comparing against themselves directly. This means there is less chance of individual differences influencing the results.
• A weakness of using repeated measure designs is that there is a high risk of order effects affecting the validity of findings. As participants are required to do multiple tasks across different conditions, there is the risk that participants may improve as they repeat the experiments. For example, if they were tested on their learning ability while music was played in one condition, when they are tested without music, the experience and practice gained from the first condition may see them improve. Researchers may then incorrectly view this improvement as due to the independent variable (IV) rather than order effects.
• Another criticism of using repeated measures is you need to create multiple different tasks or materials between the conditions. For example, you could not use the same content for participants to memorise from one condition to another in a memory test experiment. You would need to create content that was judged to be similar in difficulty which in itself would be a subjective measure. For example, having participants memorise 20 “easy” words with similar syllables in one condition, would require a researcher to spend significant time and effort in creating another set of similar words for another condition.
• There is a higher risk of demand characteristics when using repeated measure designs. This is because participants may be able to guess the purpose of the study (if it is intentionally obscured to improve the validity of findings) and then adjust their behaviour accordingly. This is more likely to happen as the same participants are used across the different conditions and they may notice the different setups and the purpose of the study. This may lead to invalid findings from the behaviour that is observed.

Matched Pairs Design

A matched pairs design involves gathering participants and testing them prior to them taking part in the study on certain characteristics . The tests allow them to be matched in pairs with someone who is deemed to have similar qualities as to them which may be relevant to the study.

The pairs may be identified as Pair Aa or Pair Bb etc.

In conducting a matched pairs design research study, one pair will take part in one experimental condition while their matched partner/pair is exposed to another experimental condition.

The results are then compared by the researcher between the conditions and treated as if they were gathered from one individual despite coming from two individuals.

Within psychological research, the most ideal matched pairs participants tend to be identical twins as they account have identical biology (as they are similar) and potentially very similar personality factors too.

Strengths and Weaknesses of Matched Pairs Design

• One strength of using matched pairs designs in research is they reduce participant variables which can affect the results. This is because the people are paired up together based on similar traits that are relevant to the study.
• Another strength of using matched pairs is that there are no order effects, unlike repeated measure design studies. This is because everyone does the experiment once and have no opportunity to learn from their previous attempts.
• Matched pairs designs can re-use the same materials/apparatus across the pairs as everyone will only be exposed to them once. This makes the setup of the experiment easier as researchers do not have to create unique set-ups across the two groups which can be time-consuming.
• A weakness of using matched pairs design is matching people on key variables is time-consuming and not always successful. Attempting to find people who can be matched requires an initial large sample to filter through and this can take a very long time to do.
• It is difficult to match people based on personality variables or filter out individual differences for certain. You can generally only match people based on fixed traits such as gender (sex), age, height etc, however, personality factors may be what determine differences in the experiments. Therefore matched pair designs can produce invalid results that are not the result of the independent variable.

Laboratory Experiments

Laboratory experiments are experiments that are conducted in a controlled setting , usually a research laboratory where participants are aware of being observed and part of a study.

Laboratory experiments tend to have high internal validity because researchers can control all the variables so the main differences between the experimental condition and control group are only the independent variable whose effect is being monitored. This allows researchers to more confidently assume that any differences between the conditions are due to the independent variable.

Strengths and Weaknesses of Laboratory Experiments

• A major strength of laboratory experiments is they have high validity. This means that researchers can be confident to a higher degree that what they are measuring is in fact due to the effect of the independent variable because this is the only difference between the experimental condition and control group.
• Another strength of using a laboratory setup is this limits the role of extraneous variables from influencing the results as researchers have complete control of the environment. This means unaccounted for outside influences are limited and makes drawing cause and effect between the IV and DV more reliable. Laboratory experiments can be checked for reliability as they are easier to replicate. Due to the artificial setup of the experiments (being in a laboratory setting), other researchers can recreate the experiment exactly to check the results for reliability. This can be harder to do with other setups.
• A weakness of using laboratory experiments is they lack ecological validity. This is because the setup of the experiment is artificial and in a completely controlled environment and the results gathered in the lab, may not generalise to real-world situations due to their contrived setup. Therefore laboratory experiments tend to lack ecological validity as the setup involved to test behaviour may not occur similarly in real life e.g. testing memory ability and learning in a lab setup is unlikely to be how people learn with or without music being present – or using a film clip to test eyewitness testimony is not realistic.
• Participants in laboratory setups may display demand characteristics and adjust their behaviour due to the contrived setup and being aware that they are being observed. Therefore the behaviour observed may lack validity as it may not be indicative of how people are likely to behave in the real world if they think they are not being observed or under supervision. Participants may, therefore, behave how they think researchers want them or what would be deemed normal with others watching, not necessarily what they would actually do.

Field Experiments

A field experiment is conducted in a more natural or everyday environment , unlike the laboratory experiment where the behaviour being measured is more likely to occur.

The field experiment can be conducted anywhere in real-world settings with researchers manipulating an independent variable to measure its impact on the dependent variable. A field experiment can include confederates that participants are unaware of also being involved to test their response in the field setting.

One key difference between a field experiment compared to a laboratory experiment, are participants may not be aware of being observed or studied. This is in an attempt to generate more realistic behaviour or responses from them that can generalise to real-world settings.

Strengths and Weaknesses of Field Experiments

• A strength of using field experiments is they are high in ecological validity as the setup and environments are more realistic. This is thought to increase more realistic responses from participants as they are not aware always aware of being observed (unlike lab settings). The argument here is field experiments have higher internal validity and the behaviours from participants can then be generalised to the wider population.
• A weakness of using field experiments is they are at higher risk of extraneous variables influencing the behaviour of participants. Researchers, therefore, have less control and cannot say with as much certainty that the behaviour they observed was in fact due to the independent variable or not.
• Another criticism of field experiments is they are difficult to replicate. Participants may be members of the public with personality factors that influence the results which are unaccounted for and the environment itself may be difficult to recreate in order to test the study for reliability in its findings. Therefore replication and reliability become an issue for field experiments.
• Another weakness of using field experiments is they raise ethical issues in regards to informed consent. This is because participants may be unaware of being observed or part of a study and this raises ethical concerns. On the other hand, this may also provide us with more realistic and valid results without demand characteristics being a potential confounding variable.

Natural Experiments

A natural experiment is conducted when ethical or practical reasons to manipulate an independent variable (IV) are not possible. It is therefore said that the IV occurs 'naturally'.

The dependent variable (DV), may however, be tested in a laboratory, for example, the effects of institutionalisation in some form, which may occur naturally due to imprisonment or disruption of attachment through the care system and how it may affect psychological development such as intellect or emotional development.

Another good example of a natural experiment is the study by Charlton et al. (2000) which measured the effects of television. Prior to 1995, the people of St. Helena, a small island in the Atlantic had no access to TV however it's arrival gave the researchers to examine how exposure to western programmes may influence their behaviour. The IV in this case was the introduction of TV which was not controlled by researchers and something they took advantage of would be practically difficult to control. The DV was measures of pro or anti-social behaviours that were assessed through the use of questionnaires, observations and psychological tests.

These types of experiments would either impractical or unethical to implement and therefore cases where this occurs naturally due to normal circumstances may be examined through natural experiments.

Strengths and Weaknesses of Natural Experiments

• One major weakness of natural experiments is the lack of control. It is more difficult to control extraneous variables which makes it difficult to establish causality.
• A strength of natural experiments is they are high in ecological validity. Due to the 'real world' environment, the results relate to everyday behaviour and can be generalised to other settings.
• Another strength of natural experiments is they often produce no demand characteristics as the participants are unaware of the experiment. Therefore the behaviour observed is more likely to be realistic and indicative of behaviour that can be generalised across wider populations.
• A weakness of natural experiments is they are difficult to replicate to double check the findings. As the conditions are never exactly the same, it becomes difficult to establish reliability in such experiments which then affects validity as causality cannot be determined. 
• Participants are often not aware of being observed or taking part in natural experiments and this raises ethical issues, in particular, informed consent. They may not wish to take part or be monitored and this is another weakness of natural experiments, although they may be debriefed after the experiment and given the option of giving consent to use the data collected from them.

One way psychologists find out about peoples behaviour is to quite simply ask them through the form of interviews. 

Interviews involve a researcher in direct contact with the participant and this could either be face to face or via phone/video call. The vast majority of interviews involve a questionnaire that the researcher records the responses on at the time of the interview. There are different forms of interviews used which vary in structure and we will look at specifically structured and unstructured interviews for GCSE psychology.

Structured Interviews

Structured interviews involve all participants being asked the same pre-set questions in the same order . The researcher is unable to ask additional questions outside of this.

The questions are often closed questions that require a yes or no response , or they can be open questions that simply require the researcher to record the participant’s response.

Open questions can be questions that begin with who, what, where, when, why and how.

These force a participant to explain their answers beyond simply saying yes or no.

Strengths and Weaknesses of Structured Interviews

• Structured interviews can be replicated far more easily than unstructured interviews as the questions are all pre-set. This helps in testing the reliability of research findings to check for consistency and validity in the conclusions drawn.
• A criticism/weakness of using structured interviews is they can be incredibly time consuming and require skilled researchers. People’s responses can also be affected by social desirability bias.
• Structured interviews gather quantitative data but lack qualitative data. When participants can only answer yes or no, this does not tell us why they think or respond this way which may be more important to understand behaviour.

Unstructured Interviews

In unstructured interviews, participants are free to discuss anything freely . The interviewer may devise new questions as the interview progresses or on the previous answers given, to explore further.

With unstructured interviews, each participant is likely to be asked different sets of questions within the interview. The questions asked in unstructured interviews may be a mix of open and closed questions.

Strengths and Weaknesses of Unstructured Interviews

• Unstructured interviews provide rich and detailed information however they can not be replicable and people’s responses cannot be easily compared.
• Unstructured interviews have the benefit of allowing participants to explain their responses which can help us understand why they think or behave in particular ways which may be more valuable than structured interviews telling us merely how they would behave.
• Unstructured interviews can be more time-consuming as there is no structure or guideline to follow in regards to how many questions are being asked. They also require more trained interviewers who are able to articulate themselves and the questions they wish to ask, unlike structured interviews which can merely be read from a list and explained more easily.

Questionnaires are an example of a survey method that are used to collect large amounts of information from a target group that may be spread out across the country.

The researcher must design a set of questions for participants to answer; people taking part in a survey are referred to as “respondents” because their answers or behaviours are in response to the questions presented. Questionnaires can be conducted face to face, via phone or video call too.

Questionnaires are similar to structured interviews as respondents all answer the same questions, in the same order and they often narrow the possible responses to closed questions (yes or no answers).

Strengths and Weaknesses of Questionnaires

• Questionnaires are practical ways for researchers to gather large amounts of information very quickly on topics where the responses are best suited for yes or no responses.
• Another strength of using questionnaires is that they can be replicated very easily as all the questions are pre-set. Responses can be gathered again to check for reliability and validity this way far more easily.
• Problems arise in the use of questionnaires when the questions are unclear or if they suggest or lead respondents into a desirable response. Responses can be affected by social desirability bias so participants may not necessarily answer truthfully which can invalidate findings.
• Another criticism of using questionnaires in research is respondents can only answer yes or no. This limits the amount of information that can be gathered but also participants may not be able to answer in certain terms yes to every presented scenario (or no). It may be that their responses only represent given situations but can be different in other situations.
• Respondents may misunderstand the meaning of questions and therefore answer incorrectly. Unlike structured interviews that allow participants to ask questions to clarify their understanding, respondents may misread or misunderstand questions and answer in a way that is not truly representative of their views.
• The researcher needs to make sure that in writing the questions, they are clear and unambiguous. This can be a difficult task to achieve and requires a great deal of time to construct questions that do not bias or lead the respondents into responses.

Case Studies

A case study is a very detailed study of the life and background of either one person, a small group of people or an institution or an event . Case studies use information from a range of sources, such as the person concerned, related family members or even friends.

Various techniques may be used such as interviewing people or observing people as they engaged in daily life. Psychologists may also use various tests such as IQ tests, personality tests or some questionnaire to produce psychological data about the target in question.

Researchers may also refer to school or work records for an individual or carry out observations of the individual or groups in question. The case study is then written up as a description of the target individual or group and interpreted information based on psychological theories.

Case studies tend to be longitudinal and follow the target over a long period of time (often many years).

Strengths and Weaknesses of Case Studies

• A strength of using case studies is they provide detailed information about individuals (or target group/institution) rather than collecting a score on a metric test from a person.
• Another benefit is case studies collect information over a long period of time so changes in behaviour can be observed and comparisons are drawn over this period to understand the changes.
• A weakness of using case studies is they target a single individual and this makes it difficult to generalise the findings to others. The situation or factors that influence this individual’s outcomes may not necessarily do the same for others due to individual differences. The data collected is also very subjective as it relies on usually peoples perceptions of things and their memories may not be so reliable over such a long period of time. There is also the risk that the researcher themselves projects their own biases onto the findings and makes their own interpretations of the content making the case study unreliable.
• There can be ethical concerns with using case studies as the people or group being followed are usually of interest because of some psychological problem. This could make them vulnerable and raise ethical concerns about whether they can give informed consent.

Observation Studies and the Observational Method

In an observational study, the researcher watches or listens to the participants engaging in whatever behaviour is being studied and records their behaviour . In most natural observations, people are observed in their normal environments without interference from the researcher.

In some studies, a researcher may cause something to happen to gauge the responses of people and record these.

Here’s an example of one such study:

A nurse is called by a “doctor” via telephone and instructed to give medicine to a patient which is against the rules. The study was conducted in the nurses natural setting of the hospital and researchers then observe whether the nurse follows this instruction or not.

In some studies, the data may also be collected in a “laboratory setting” although this may not necessarily be a laboratory. This may be a natural setting that has been organised by the researcher to make it easier to observe the targets.

Strengths and Weaknesses of Observation Studies and the Observational Method

• What people say is often very different from what they may do in a given situation. The observational method is high in ecological validity and its use is very suitable for social behaviours as it allows researchers to gauge peoples true responses. If participants were asked about their behaviour prior, they may give socially desirable responses which may not be what they would really do and observational studies allow us to see true behaviour without this bias.
• The behaviours observed in observational studies have higher external validity as they can be more easily generalised. Unlike laboratory studies that test participants under contrived circumstances (e.g. memorising lists of words to test memory), observational studies and their setup are more natural providing more ecologically valid results.
• A weakness, however, is although researchers see and record behaviour in an observational study, they do not know why the behaviour happened. This then requires the researcher to make a judgement on its cause which may be riddled with bias or may simply be incorrect.
• Participants or subjects may become aware of being observed and thus change their behaviour leading to researchers recording incorrect responses. Also, the researcher themselves may make a mistake recording the behaviour which can invalidate findings.
• Observational studies also raise ethical issues particularly around informed consent as participants are usually not aware of being observed or part of a study. Informing them prior may lead to their behaviour altering when they are aware of being observed however not informing them raises ethical issues of privacy and lack of consent.

Categories of behaviour

In order to make sure that accurate records of behaviour are made, researchers use categories of behaviour systems.

If researchers wanted to observe “playground behaviour”, researchers would not necessarily know what they were looking for in this definition or what may be classified as “playground behaviour”. The observers would need to know what they are looking for to make accurate recordings and therefore behavioural categories are created to make it clear what behaviours are to be recorded.

Inter-observer reliability

When an observation study is conducted, observers record the number of times certain behaviours occur (usually in the form of a tally chart).

This record of the number of incidents for the different behaviours needs to be accurate and ensure that the observer is recording the correct behaviour within the correct categories.

In observation studies, observers may miss the behaviour and so accuracy of recording the behaviour becomes an issue as it cannot be seen again in live environments. A solution to this problem is to design a record sheet with the pre-defined suitable behaviour categories and then have two observers independently observe the targets at the same time and location . Each would then record what they see in their own individual sheets independently from the other.

At the end of the study, the observers may compare their record sheets to check for consistency . If the sheets have been recorded correctly, they should have matching or very similar recordings of their observations. If this occurs, they have established inter-observer reliability. If the record sheets are considered vastly different, this would mean the study lacks inter-observer reliability and the results lack validity as they are not measuring what they are supposed to measure accurately.

What is a Correlation?

For this section of Research Methods, we need to know about the following in relation to Correlations:

• An understanding of association between two variables and the use of scatter diagrams to show possible correlational relationships.
• The strengths and weaknesses of correlations.

A correlation is quite simply a relationship between two variables. There are 3 types of correlations which are:

• Positive correlation,
• Negative correlation
• Zero correlation

With positive and negative correlations, the relationship is seen as a “cause and effect” relationship whereby one variable has a direct impact on the other . Correlations form part of a statistical technique to analyse and display the possible relationship between the two variables.

Let’s work through a few subjective examples for each: Let’s assume there is a correlation (relationship) between the two variables age and beauty. As people get older they may be seen to be more beautiful. This would be considered a positive correlation because both the variables increase together .

If however people disagreed and thought that as people age and get older, they are less beautiful, this would be a negative correlation. This is because as one variable increases, the other one decreases which in our case would be age increasing while beauty decreases.

The third way of looking at this is thinking that age has no effect on perceived beauty. As people get older you may think this has no bearing on a person’s beauty so the two variables would be seen as having zero correlation.

Below we have some examples of scattergrams that give you an idea of how each correlation would look if presented to you. You may sometimes be asked to draw a line of central tendency too within a correlation; all this means is you draw a line down the middle of all the correlations with equal amounts on either side of it.

Positive Correlations

Negative correlations, zero correlation, strengths and weaknesses of correlations.

• Correlational research can be very useful as they allow a researcher to see if two variables are connected in some way. Once a relationship has been established between two variables, a researcher can then use an experiment to try and find the true cause of the correlation.
• Correlational research can be used in situations where it may be unethical or impossible to carry out an experiment. For example, if we wanted to check for the relationship between smoking and cancer, this would be unethical to test (asking people to smoke to see if they develop cancer). However, plotting the rates of cancer developing in people who already smoke can help us establish links between these two variables. This knowledge can then be helpful in influencing future research.
• A weakness of using correlations is although this type of tool can tell us if two variables are related, it does not tell us which of the two variables caused the relationship. It is also possible that there may be third unknown variables that lay in between and influence the two we measure in research which may be the actual cause.
• For correlational research to be helpful, we first need to gather large amounts of data to establish the pattern in the scattergraph. This means researchers are required to make lots of measurements of both variables so that the patterns in the data can be reliably established. Using correlational research for small populations is not reliable so it can be very time-consuming establishing a large data set.

Research Procedures

What the GCSE Psychology specification says you need to learn for this section on Research Procedures:

• Standardised procedures
• Instructions to participants
• Randomisation,
• Allocation to conditions

Counterbalancing

• Extraneous variables (including explaining the effect of extraneous variables and how to control for them).

Standardised Procedures

When conducting experiments, researchers need to ensure that standardised procedures are used.

Standardised procedures are a set of sequences that apply to all the participants when necessary to ensure the experiment is unbiased . Standardised procedures allow the researcher to try and control all the variables and events so the results of the experiment can be safely attributed to the independent variable.

Instructions to Participants

When standardising procedures, another issue researchers need to be mindful of is how instructions to participants are put across to make sure they know what to do but without biasing the study in any way. This can include verbal and written instructions.

Instructions can be interpreted in a way that can influence their performance and these can become extraneous variables. For example, if instructions were worded with leading questions, this may cause participants to answer in one particular way. If instructions are ambiguous, this can also affect the results of the study.

To address this issue, the usual practice is to write as much information as possible for participants and ensure they all receive this same information. This is usually done in sections as follows:

• Briefings: this is where participants are encouraged to participate with a log of what is discussed to gain their consent. This can include ethical information about consent, anonymity, the right to withdraw etc.
• Standardised instructions are given: these are clear instructions given to each participant explaining their role and what they need to do.
• Debriefing: at the end of the study, participants are given a detailed explanation about the aims of it, what their role was and why they were given their tasks or roles. Ethical issues are also raised again with participants given the opportunity to withdraw their data/contributions if they feel unhappy about their performance or participation.

Randomisation

Randomisation simply means to make sure there are no biases in the procedures .

Let’s use our music and learning example again for a moment to highlight how randomisation may be implemented in a psychological study.

Participants are being tested on their ability to learn through the use of 20 random words they are presented with. All the words are considered to be of equal difficulty because they are everyday nouns with only six letters. The researcher has to decide which order they should be presented to each of the participants in the study however instead of the researcher determining the order, randomisation is used.

All 20 words are written down on a piece of paper and put into a hat. They are then randomly selected one after the other with their order being written down in which they have been selected. This order is then determined to be the order to which all participants will be exposed within the experiment.

Using randomisation, all the words had an equal chance of selection and now with an order established, all participants will be exposed to them in the same way. Randomisation can be implemented in a number of ways within an experiment to filter out biases and you may be given a question on how to best implement this or its benefits.

Another major issue researchers face, is how to allocate the participants to the experimental condition or control group.

To reduce researcher bias, two methods used are random allocation and counterbalancing .

Random Allocation

When the design of the study uses an independent group design, the researcher can use random allocation to avoid any potential researcher bias.   Participants can be randomly selected in turns for either condition A or condition B by pulling their name out of a hat for example.

A similar method can be employed if the design of the experiment is a matched pairs design. Participants can be randomly allocated to their pairs by them pulling out the letters for each pair from a hat e.g. the two people who pull out A+a from a hat form a pair, the same with B+b, C+c etc and so forth.

For experimental designs such as the repeated measures design, all the participants are required to take part in the experiment for both conditions. The problem with this is that order effects can occur whereby participants learn from experience and thus do better in all the following conditions after their initial one.

Counterbalancing helps balance out order effects by splitting the group of participants into two groups. One half will then complete condition 1 while the other half complete condition 2. 

After completing this, they swap and complete the opposite condition so those who completed condition 1, then move on to complete condition 2, those that completed condition 2, go on to complete condition 1.

Using counterbalancing does not get rid of order effects but allows for the effects of it to be balanced out equally between the two conditions for participants and thus providing more valid results.

Ethical considerations

For Ethical Considerations, the specification states you need to know the following:

• Ethical issues in psychological research as outlined in the British Psychological Society guidelines
• Ways of dealing with each of these issues.

This next section focuses on all the ethical considerations based on the British Psychological Society guidelines and ways in which each can be dealt.

Ethical issues arise when there are two conflicting points of view;

• One is what the researcher needs to do in order to conduct a useful and meaningful study
• The second is the rights of the participants which need to be considered .

Ethical issues are therefore all the conflicts that arise about what is acceptable to do as part of the research.

As part of your GCSE psychology course, you need to be able to highlight ethical concerns and generate ways in which to deal with them. You may also be given a scenario where you need to highlight the relevant concerns and comment on how to deal with them.

The Code of Ethics and Conduct (2009) and Code of Human Research Ethics (2014) from the British Psychological Society underpin the activities of all practising psychologists.

What Are The British Psychological Society Guidelines?

When research is conducted by any practising psychologist, The Code of Ethics and Conduct (2009) and Code of Human Research Ethics (2014) will underpin their work. 

The British Psychological Society (BPS) guidelines explain what is required:

• Participants should be respected as individuals and unfair or prejudiced practices are to be avoided.
• The data collected should also be confidential and anonymised so participants cannot be identified from the research.
• Participants should have also given informed consent and know fully what they are consenting to. They should also be told at the beginning what the study is about prior to taking part.
• Deception must be avoided although the BPS recognises that some studies are not possible without this to gather meaningful results. Any deceptions that do take place must be explained to participants as soon as possible once the study concludes.
• They should also be aware of their right to withdraw from the study at any time.

Psychologists should maintain high standards in their professional work which includes:

• Being aware of the code of conduct
• Recognising that ethical dilemmas will inevitably arise and seeking to resolve them
• They should only give advice if they are qualified to do so and not trying to do things that are beyond their competence.
• Staying within the law if ethical principles conflict with the law but try to maintain the ethical principles as far as possible
• Monitoring their own health and lifestyle to recognise times when they may be unable to carry out their work competently

Responsibility

Responsibility within the British Psychological Society (BPS) is generally about avoiding harm to clients, avoiding misconduct that would bring psychology into disrepute and looking out for other psychologists that may be breaching these guidelines.

The BPS states researchers should:

"Consider all research from the standpoint of research participants, for the purpose of eliminating potential risks to psychological well-being, physical health, personal values, or dignity"

This can be done by:

• Ensuring researchers protect participants from physical and psychological harm.
• Making sure the risk of physical or psychological harm is no greater than what one would expect from everyday life and their wellbeing should not be at risk.
• At the end of the experiment, participants should be debriefed at the end of the investigation so they fully understand the true aim of the study. This would then allow them to make an informed decision about whether they wish to withdraw their results.

Informed Consent

Informed consent means revealing to the participant the real aims of the study or telling them what will happen within the study. This becomes an ethical issue because revealing the true aims or details may lead to the participants adjusting their behaviour which could lead to invalid results.

For example, if we wanted to study whether people are more likely to obey a male or female as part of research into obedience, revealing the aims of this study will almost certainly affect their behaviour and invalidate findings.

Researchers may therefore not always give out the full details of the study however this means participants can not give their full informed consent. From a participants point of view, they should be told what they are required to do in the study so they can make an informed decision about whether they wish to take part.

This became a basic human right that was established during the Nuremberg war trials after the second world war. During the war, Nazi doctors conducted various experiments on prisoners without their consent and the war trials afterwards decided that consent should become a basic human right for participants to be involved in a study.

Epstein and Lasagna found that only a third of participants volunteering for experiments really understood what they had agreed to take part in despite giving informed consent. This demonstrates that even if researchers sought to and obtained informed consent, this does not always guarantee that participants understand what they are involved in or doing.

How to deal with ethical issues of informed consent

• Participants could be asked to formally indicate their agreement to take part based on information concerning the nature and purpose of the study and how their role fits in. 
• Presumptive consent may also be gained; this can be done by asking a group of people whether they feel a planned study is acceptable and assume that the participants themselves would have felt the same if given the opportunity to say so.
• Researchers can offer the right to withdraw at any stage of the study to participants so if at any stage they feel uncomfortable or do not wish to continue, they can exit the research.

Some experiments require deception about the true aims of research otherwise participants might alter their behaviour and the study’s findings become meaningless . A distinction could be made in some cases between withholding some details about the study (reasonably acceptable) compared to deliberately providing false information (less acceptable).

From the participant’s point of view, deception would be unethical and thus they should not be misled without good reason.

An issue with deception is it prevents participants from giving informed consent . Participants may agree to take part without fully knowing what they have agreed to and become quite distressed by the experience. Baumrind (1985) argued that deception was morally wrong based on three generally accepted ethical rules within western society: the right of informed consent, the obligation of researchers to protect the welfare of participants and the responsibility of the researcher to be trustworthy.

Others have argued that deception can be harmless in some studies i.e. testing memory, and deception may be necessary to gain meaningful insights that would not be otherwise possible.

How to deal with ethical issues of deception

• The need for deception in research could be approved by an ethics committee which weighs up the potential benefits of the research, against the costs to participants.
• Participants should be fully debriefed after the study and given the opportunity to request that their data is withheld.

The Right to Withdraw

Participants would deem the right to withdraw from an experiment as important. If a participant begins to feel distressed or uncomfortable, they should have the right to withdraw from the study. This becomes more important particularly if they have been deceived about the nature of the study or their role.

From a researchers point of view, participants being able to withdraw midway through a study could bias the results in some way when comparing the results of those that stayed.

Within some experiments, participants are offered financial payments for completing the study and withdrawing is compromised because they may not get paid and thus feel like they can not withdraw.

Confidentiality

A researcher may find that maintaining confidentiality can be difficult as they wish to publish the findings. They may guarantee anonymity and withhold the participants’ names, but even then it may be evident for some who the participants are.

In some locations or communities which are remote or the population is low, naming even the geographical area can identify the individual. The Data Protection Act makes confidentiality a legal right and it is only acceptable for a person’s data to be recorded if it does not make it available in a form that can make the people identifiable.

To tackle this researchers should not record any names or personal details about the participants using numbers or fake names instead.

Privacy may be difficult to accomplish from a researchers point of view, particularly when studying participants without their awareness.

Participants may feel that they should not be expected to be observed or watched by others in some situations e.g. within the privacy of their own homes although not when in public areas such as a park.

To tackle this researchers should not observe anyone without their informed consent unless it is in a public place where this may be expected to some degree. Participants could also be asked to give their retrospective consent or withhold the data entirely.

Data Handling

What the GCSE Psychology specification says you need to learn for this section:

• Quantitive and qualitative data
• Primary and secondary data
• Computation
• Descriptive statistics
• Interpretation and display of quantitative data
• Normal distributions

There are two types of data research studies collect which are:

• Quantitive data
• Qualitative data

What is Quantitive data?

• Primary data is data that has been collected firsthand from the source (participants) directly by researchers. The majority of data collected in psychological research will be primary data.
• Secondary data is data that has been already published and simply used by researchers in their own work.

Strengths and Weaknesses of Quantitive Data

• Quantitive data tends to be objective and easy to measure for researchers.
• Precise measures are used,
• The data is high in reliability and can be checked through replication.
• The data can be more easily examined to check for patterns through the use of correlations and presented in the form of scattergrams.
• Weaknesses of quantitive data include the possibility that meaningful details could be lost or lacking as researchers focus on a narrow set of responses or pre-defined questions people answer.

What is Qualitative Data?

Qualitative data is descriptive data that is non-numerical. This type of data provides detailed information which can provide insights into the thoughts and behaviours of individuals because the answers are not restricted to yes or no responses. For example, in an observational study, researchers may describe what they see and this would be deemed a form of qualitative data.

Qualitative data tends to be collected through the use of open questions (questions that begin with who, what, where, when, why or how) that encourage participants to explain themselves. This is done usually through questionnaires or unstructured interviews.

Qualitative data cannot be counted or quantified as easily although it can be placed into categories to count the frequency in which it is reported to occur. For example, we may be able to count how many times participants in Milgram’s study reported being stressed or worried.

However as the responses from participants can be completely subjective to them, the data can be incredibly varied based on their responses and difficult to quantify or generalise with any meaning. 

Strengths and Weaknesses of Qualitative Data

• A major strength of qualitative data is it tends to be rich in detail.
• Another strength is qualitative data can help researchers understand peoples attitudes, thoughts and beliefs which may better explain their behaviour rather than them having to guess.
• A weakness of using qualitative data is it tends to be completely subjective.
• Qualitative data tends to also be an imprecise measure that is difficult to quantify.
• Another criticism of qualitative data is the difficulty in checking for reliability as participants all give subjective responses. This makes it difficult to generalise to other people.

What Is Primary and Secondary Data?

What is the mean, median, mode and range.

There are three types of averages that can be calculated from the raw data obtained from studies which allow researchers to identify patterns in the behaviour.

These three are:

• The mean average
• The median average

You can also work out the range although this is not an average.

The Mean Average

The Mean Average is calculated by adding together all the values in a set of scores and then dividing that number by the number of values in the set .

For example, if we wanted to work out the mean average for what Brad Pitts score would be on a beauty scale from us questioning 12 people, we would take their scores, add them up and then divide them by the number of people in the study (in our case, this would be 12).

Let’s work through an example assuming that the beauty score is out of 10:

So to work out the mean average we would need to add up all the scores all 12 people have given – this would then be:

• 7 + 8 + 7 + 8 + 9 + 7 + 10 + 8 + 7 + 6 + 9 + 8 = 94
• 94 is the total score of all 12 participants. We then divide this by 12 (the number of participants)
• 94 divided by 12 = 7.83

Brad Pitt’s mean average score would be 7.83/10 (out of 10)

The Median is the middle value of a set of scores.

• To calculate the median you must arrange all the values in order from the lowest to the highest .
• Then you must find the middle value . If there isn’t an obvious middle value due to an even number set, you work out the midpoint of the of the two middle values.

Let’s work out the median value using the example above; we must first order the numbers from lowest to highest.

6, 7, 7, 7, 7 , 8, 8, 8, 8, 9, 9, 10

Having ordered the numbers we can see that the midpoint is 8 either side. Therefore the median in the example is 8.

The Mode is the most frequently occurring value in a set of scores.

Sometimes there may be no mode (if no number occurs more frequently than another) or there can be more than one mode.

Let’s work out the mode using the example above again by first writing down all the scores; we must first order the numbers from lowest to highest.

6, 7, 7, 7, 7 , 8, 8, 8, 8 , 9, 9, 10

We can see that the mode is 7 and 8 because they both appear the most which is 4 times.

The Range is the numerical difference between the highest and lowest set of scores.

So using our example above we can see that the highest score is 10 and the lowest score is 6.

We therefore minus 6 from 10 as follows:

The range is therefore 4.

Ratios, Fractions, Percentages

This section focuses on recognising and using expressions in decimal and standard form.

These include:

• Fractions/decimals

Percentages

A ratio is a way of comparing the amounts of something between each other and this is usually expressed in its simplest form.

If we had 15 boys and 12 girls in one class and we wanted to compare this as a ratio, this would be 15:12.

When we break this down into its simplest form this would be 5:4 because we can divide both sides by 3.

Fractions and Decimals

A fraction is a way of expressing a part of a whole number.

For example, if we had a group of 20 boys and 15 of those produced the action of running which we wished to express, the fraction would be 15/20 or 3/4 in its simplest form.

As a decimal, this may be expressed as 0.75 as the total or whole amount is always represented as 1. The number of boys that did not express running would, therefore, be 0.25

Percentages are a way of expressing a fraction of a hundred which is considered the full amount.

So 50/100 would be expressed as 50% (percent). This is sometimes used in psychology to express how often something happens e.g. running occurred 75% of the time.

So using the example before, if we had a group of 20 boys and 15 of them were seen to be running and we wanted to work out the percentage of this, we could calculate it in the following way:

• 15 x 100 divided by 20 (total no. of people) = 75%.

So to rephrase:

• 15 (boys) x 100 (the whole amount) divide by 20 (the total number of boys) = 75%

Bar charts are used to display data that is in categories.

Each bar represents a separate category with them labelled across the x-axis which is at the bottom (horizontal). The frequency or amount for each category is labelled on the y-axis which runs along the side (vertical). The bars drawn should not touch and be separated from one another.

Here’s a picture of the one we used earlier to measure the hypothetical study of beauty:

Histograms are used to present data that are continuous measurements such as test scores or even height.

The continuous scores are on the x-axis across the bottom and the frequency of these scores are on the y-axis. Histograms have no spaces between the bars (unlike bar charts) as the data is continuous.

Here’s an example below:

Scattergrams

We’ve already looked at scattergrams when discussing correlations earlier.

Here is an example of a Scattergram showing a positive correlation below – notice how all the recording measure along an invisible line almost going diagonally across:

Normal Distributions

The normal distribution is the predicted distribution when considering an equally likely set of results.

On a graph, this shows as a bell-shaped curve encompassing the mean, median and mode .

For example, in an IQ test, most scores for the whole population would be around the mean average with decreasing scores away from this for those with lower IQ’s as well as higher.

In a normal distribution the mean, median and mode scores tend to be of very similar value when plotted to produce a distinctive curve. The curve shape is what we call the normal distribution curve.

Here is an example of a normal distribution curve below:

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Hypotheses Exam Questions (GCSE Statistics)

Subject: Mathematics

Age range: 14-16

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29 February 2024

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Exam Questions and mark scheme on Hypotheses for Edexcel GCSE (9-1) Statistics. All questions are from the Edexcel GCSE Statistics specification.

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The Sapir-Whorf Hypothesis: Thinking Depends on Language ( AQA GCSE Psychology )

Revision note.

Psychology Content Creator

Language determines thought

• Piaget (see here ) believed that language depends on thought i.e. it is not possible to have the words available or to understand language without context (thoughts being a context in which language can ‘take root’)
• The Sapir-Whorf Hypothesis (SWH) takes the opposite view i.e. language precedes (and in turn produces) thought
• Language determines thought, therefore if the language you speak does not have specific words/ vocabulary for an object/idea/event then you will not be able to think about such an object/idea/event
• People from different cultures will think differently based on their cultural experiences - and this will be reflected in the language they use, which is known as l inguistic relativity
• The language a person uses determines their worldview and perspective i.e. language comes first and thoughts depend on the structure, content and quality of whichever language is learned from birth
• An example of linguistic relativity is the Inuit Eskimos and their words for snow : qanik (falling snow); aputi (ground snow); aniu (drinking water snow) compared to the one word for snow in English

How many types of snow? The Inuit Eskimos have several different words for the English language word 'snow'.

The terms ‘strong’ and ‘weak’ relating to the two different versions of the SWH are nothing to do with actual strength or quality: they simply refer to the degree to which language is assumed to influence thought. So, the ‘strong’ version assumes that language directly determines thought; the ‘weak’ version suggests that language influences thought i.e. in a more ‘gentle’ and indirect way.

Language influences thought

• It does not insist that language determines thought
• It is possible to have a concept /thought about something without having direct experience of it e.g. most people will have words to describe the experience of being in prison - ‘ banged up’, ‘screws’, ‘grass’ - without having ever been in prison
• The example of Inuit ‘snow’ words is not a completely alien concept to a native English speaker: qanik (falling snow) is not difficult to visualise or understand i.e. language helps to shape thought
• Sapir-Whorf and other theorists and researchers agree that the weak version of the SWH provides a better understanding of the relationship between language and thought

Does language determine thought or merely influence thought?

This topic is a little tricky in terms of the terminology/technical words involved e.g. ‘linguistic relativity’. Make sure that you have learned (and understood) these terms fully before the exam as a confident use of terminology will help to elevate your mark.

Evaluation of the Sapir-Whorf Hypothesis

• The weak version of the SWH has been supported by research e.g. Kay & Kempton (1984)
• The SWH has some external validity as it assumes that culture affects language and that this in turn influences thought i.e. it makes sense in terms of real-world experience
• The issue of b ilingualism/multilingualism   highlights the limitations of the strong version of the SWH as people who speak more than one language fluently do not necessarily think differently per language
• The idea that the Inuit have many words for snow has been disputed plus it is also argued that there is more than one word for snow in English ( hail, slush, sleet etc.) which makes the SWH lack validity to some extent (Pullum, 1989) 

Worked example

Here is an example of a question you might be asked on this topic - for AO1.

AO1: You need to demonstrate knowledge and understanding of key concepts, ideas, theories and research.

AO2: You need to apply your knowledge and understanding, usually referring to the ‘stem’ in order to do so (the stem is the example given before the question)

AO3: You need to analyse and evaluate key concepts, ideas, theories and research.

After each featured question there is a ‘model’ answer i.e. one which would achieve top marks in the exam.

Question: Which one of the following statements about the strong version of the SWH is correct:

A Thought determines language

B Language influences thought

C Language determines thought

D Language depends on thought  

Model answer :

• The answer is C, Language determines thought

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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.