Aims and Hypotheses in Psychology: Directional vs Non-Directional

In standard psychology papers, 96% of first hypotheses are supported. In Registered Reports, where the hypothesis is fixed before any data exist, only 44% are (Scheel et al., 2021). How a hypothesis is written, and when, matters.

Every psychology study starts with a question, but a question on its own cannot be tested. “Does music affect revision?” is interesting, yet no set of results could ever prove it wrong. Before collecting any data, a researcher has to turn that question into two things: an aim, which says what the study is about, and a hypothesis, which makes a precise prediction that the results can either support or contradict.

Aims and hypotheses are among the most examined skills in A level research methods. The AQA specification asks students to state aims, explain “the difference between aims and hypotheses”, and write hypotheses that are “directional and non-directional” (AQA, 2015). Exam questions regularly give a short scenario and ask for a hypothesis to match it. They also link directly to the statistics: whether a hypothesis is directional decides which column of a critical value table you read, and so whether a result counts as statistically significant.

This guide explains aims, alternative and null hypotheses, and directional and non-directional hypotheses in plain language. It uses real studies to show how each is written, sets out a step-by-step method for writing your own, and ends with a hypothesis builder and a practice quiz.

What Is a Hypothesis in Psychology?

A hypothesis in psychology is a precise, testable statement that predicts what a study will find. It names the variables involved, says how they will be measured, and states the expected outcome, such as a difference between two groups or a relationship between two measures. A hypothesis is written before data collection, and the results either support it or do not.

The word “testable” is doing a lot of work. A statement is only a hypothesis if there is some possible result that would show it to be wrong. “Some people remember things better than others” is true, but no study could contradict it. “Participants who revise in silence will recall more words out of 20 than participants who revise with music playing” can be contradicted: if the music group recalls as many words, or more, the prediction has failed. That possibility of failure is exactly what makes it scientific.

Several terms appear in almost every exam question on this topic, and each has a precise meaning.

Why psychology needs hypotheses

Psychology needs hypotheses because they commit the researcher to a prediction before the data can influence it. Without a hypothesis stated in advance, it is easy to look at a set of results, find whatever pattern happens to be there, and then claim that was the point all along. That habit has a name, HARKing, and it is discussed later in this guide.

Hypotheses also connect a study to theory. A theory, such as social learning theory, makes broad claims about behaviour. A hypothesis turns one of those claims into a specific prediction about one situation that can be checked. If the prediction holds, the theory gains support; if it fails repeatedly, the theory is in trouble. This is the hypothesis-testing part of what AQA calls the features of science (AQA, 2015).

Aims vs Hypotheses: What Is the Difference?

The difference between an aim and a hypothesis is that an aim describes the purpose of a study in general terms, while a hypothesis makes a specific, testable prediction about the result. An aim usually starts with “To investigate…” and names the broad topic. A hypothesis is a statement of what will happen, with the variables operationalised so the prediction can be checked against data.

A useful way to remember the difference: the aim is the question, the hypothesis is the answer you expect. The aim “to investigate whether caffeine affects reaction time” does not say what will happen. The hypothesis commits to an outcome and says exactly how it will be measured.

How to write an aim

An aim is written by naming the variables the study is interested in and the kind of link being investigated. For an experiment, that means the effect of one variable on another. For a correlation, it means the relationship between two variables. Aims do not need to be operationalised, and they should not make a prediction.

• Experiment: To investigate the effect of background music on the recall of a word list.
• Correlation: To investigate the relationship between hours of sleep and exam performance.
• Observation: To investigate how often toddlers share toys during free play.

The classic studies show the same pattern. Loftus and Palmer (1974) set out to investigate whether the wording of a question affects eyewitness estimates of speed. That is an aim. The hypothesis is the specific prediction that one verb will produce higher estimates than another. You can read how the study was run in our guide to Loftus and Palmer’s car crash experiment.

Does the aim or the hypothesis come first?

The aim comes first. A researcher decides what they want to investigate, reads the previous research on it, and only then writes a hypothesis. The reading matters, because it is previous research that tells the researcher whether they can predict a direction. In a psychology report the aim and hypotheses sit together at the end of the introduction, after the background research that justifies them.

Types of Hypothesis in Psychology

Psychology uses two kinds of hypothesis in every study, and one of them comes in two forms. There is the alternative hypothesis, which predicts an effect, and the null hypothesis, which predicts no effect. The alternative hypothesis can then be directional, predicting which way the effect goes, or non-directional, predicting only that there will be an effect.

The three exam boards use slightly different words for the same ideas. AQA lists “directional and non-directional” hypotheses (AQA, 2015). OCR lists null hypotheses, alternative hypotheses, and “one-tailed (directional)” and “two-tailed (non-directional)” hypotheses (OCR, 2015). Edexcel refers to “alternate, experimental and null” hypotheses and to directional and non-directional hypotheses and tests (Pearson Edexcel, 2015). Whatever the board, the ideas are the same.

The Alternative Hypothesis (Experimental Hypothesis)

The alternative hypothesis is the prediction that there will be a difference between conditions, or a relationship between variables. It is the hypothesis the researcher usually expects to be true. When the study is an experiment, the alternative hypothesis is often called the experimental hypothesis; in other designs, such as correlations, “alternative hypothesis” or “research hypothesis” is used instead.

An alternative hypothesis for an experiment names the independent variable, the dependent variable and the expected difference:

• Participants who sleep for eight hours will recall more words out of 30 than participants who sleep for four hours.
• There will be a difference in anxiety scores out of 50 between students who do ten minutes of breathing exercises before a test and students who do not.

An alternative hypothesis for a correlation names the two co-variables and the expected relationship:

• There will be a positive correlation between hours of revision per week and mock exam score out of 100.

The words “alternative” and “experimental” confuse many students. “Alternative” makes sense once you know that the null hypothesis is the one being tested: the alternative hypothesis is the alternative to the null. If the evidence is strong enough to reject the null, the alternative is accepted in its place.

The Null Hypothesis in Psychology

The null hypothesis is the prediction that there will be no difference between conditions, or no relationship between variables. Any difference or relationship that does appear in the data is assumed to be due to chance. The null hypothesis is the one that statistical tests actually test, so every study has one, even if the report never writes it out.

• Experiment: There will be no difference in the number of words recalled out of 30 between participants who sleep for eight hours and participants who sleep for four hours.
• Correlation: There will be no correlation between hours of revision per week and mock exam score out of 100.

The term comes from the statistician Ronald Fisher. In The Design of Experiments, Fisher (1935) introduced the “null hypothesis” and made a point that still shapes how results are described: the null hypothesis is “never proved or established, but is possibly disproved”. An experiment, in his view, exists to give the facts a chance to disprove it.

Why the null hypothesis is the one that is tested

The null hypothesis is tested because it is exact, while the alternative is not. “No difference” is one precise claim. “There will be a difference” covers an endless range of possible differences, large and small, so there is no single version of it to check. Fisher (1935) made exactly this argument: a hypothesis has to be free from vagueness to be tested, and “no effect” is the claim that meets that standard.

A statistical test works out how likely the results would be if the null hypothesis were true. If that probability is very small, usually less than 5% (p ≤ 0.05), the researcher concludes the results are unlikely to be due to chance and rejects the null hypothesis. This follows the logic of falsification: a claim can be shown to be false, but no number of results can prove a general claim true.

Accept, reject or retain?

A significant result means the null hypothesis is rejected and the alternative hypothesis is accepted. A non-significant result means the null hypothesis is retained. Some textbooks say “accept the null hypothesis”, but “retain” is more accurate. A non-significant result does not show that there is no effect. It shows only that this study did not find enough evidence of one, perhaps because the sample was small.

Directional vs Non-Directional Hypotheses

A directional hypothesis predicts the direction of the result, such as which group will score higher or whether a correlation will be positive or negative. A non-directional hypothesis predicts that there will be a difference or a relationship, but does not say which way it will go. Directional hypotheses are also called one-tailed, and non-directional hypotheses two-tailed.

Directional hypothesis: definition and examples

A directional hypothesis states which way the results will go. In an experiment, it says which condition will produce the higher or lower score. In a correlation, it says whether the relationship will be positive, where both variables rise together, or negative, where one rises as the other falls.

• Participants who are asked how fast the cars were going when they “smashed” will give higher speed estimates, in miles per hour, than participants asked about cars that “hit” each other.
• Students who test themselves with flashcards will score higher on a 30-mark biology test than students who reread their notes.
• There will be a positive correlation between the number of friends a child has and their score on a social skills rating scale out of 20.

A quick check: if the hypothesis contains a comparison word such as “more”, “less”, “higher”, “lower”, “faster” or “slower”, or the word “positive” or “negative”, it is directional.

Non-directional hypothesis: definition and examples

A non-directional hypothesis states that there will be a difference or a relationship without predicting its direction. Either outcome would support it: group A scoring higher than group B, or group B scoring higher than group A.

• There will be a difference in the number of words recalled out of 20 between participants who learn the list underwater and participants who learn it on land.
• Participants who work in a quiet room will differ in the time taken, in seconds, to complete a jigsaw from participants who work in a noisy room.
• There will be a correlation between age in years and reaction time in milliseconds.

The giveaway words are “difference”, “differ”, “relationship” and “correlation” with nothing to say which way. Be careful, though: “there will be a difference” and “there will be no difference” look alike, and the second is a null hypothesis, not a non-directional one.

When to use a directional hypothesis

A directional hypothesis should be used when previous research, or a well-supported theory, gives a clear reason to expect the result to go one particular way. A non-directional hypothesis should be used when there is no previous research on the topic, or when earlier studies have found conflicting results. The direction must be decided before the data are collected, never after looking at them.

Bandura, Ross and Ross (1961) is a good example of directional hypotheses grounded in earlier work. Their paper states that children who watched an aggressive adult would reproduce aggressive acts like the model’s, and would differ from children who saw a calm model or no model. They based this on a previous study showing that children readily imitate an adult who is present (Bandura and Huston, 1961). They also predicted that boys would be more likely than girls to imitate aggression, reasoning that aggression is a “highly masculine-typed behavior”. Every one of their predictions stated a direction because they had reasons to expect one.

The results show why a direction is a real commitment. The prediction that watching aggression increases aggressive behaviour was, in their words, “clearly confirmed”. The prediction that boys would imitate aggression more than girls was “only partially confirmed”: boys copied more of the model’s physical aggression, but the groups did not differ in verbal aggression (Bandura et al., 1961). There is more on the study in our guide to Albert Bandura and the Bobo doll experiment.

Outside A level, statisticians hold one-tailed tests to a stricter standard. Ruxton and Neuhäuser (2010) argue that researchers should only use one if they can explain why they are interested in an effect in one direction and not the other. They also need to explain why they would treat a large result in the unexpected direction the same as no result at all. That second point is the real cost of a directional hypothesis, explained next.

Why the choice matters: one-tailed and two-tailed tests

The choice between a directional and a non-directional hypothesis matters because it decides which critical value is used to judge significance. A directional hypothesis is tested with a one-tailed test, and a non-directional hypothesis with a two-tailed test. At the same significance level, a one-tailed test sets an easier target, because all of the 5% is placed on the side of the prediction.

Here is a worked example using the Wilcoxon signed-rank test, where the calculated value T must be equal to or less than the critical value to be significant. With 10 participants at p ≤ 0.05, the critical value is 10 for a one-tailed test and 8 for a two-tailed test.

The same data give a significant result under one hypothesis and a non-significant result under the other. That is why the direction must be fixed in advance. If a researcher could look at the data and then decide their hypothesis had been directional all along, they could turn near-misses into significant results. The same rule applies to the sign test, the Mann-Whitney U test and Spearman’s rho, all of which have separate one-tailed and two-tailed critical values.

The flip side is the cost mentioned above. A one-tailed test looks in one direction only. If a researcher predicts that music will improve recall and it actually makes recall dramatically worse, a one-tailed test cannot count that as significant, however large the effect. A directional hypothesis buys an easier threshold at the price of being blind to surprises.

How to Write a Hypothesis in Psychology

To write a hypothesis in psychology, identify the variables, operationalise them so they can be measured, decide whether previous research justifies a direction, and then write one clear sentence that predicts the outcome. Finally, write the matching null hypothesis. The same five steps work for any experiment or correlation.

1. Identify the variables. For an experiment, name the independent variable (what is changed) and the dependent variable (what is measured). For a correlation, name the two co-variables.
2. Operationalise each variable. Say exactly how it is measured or manipulated: “words recalled out of 20”, “time in seconds”, “score on a 10-point scale”.
3. Choose directional or non-directional. If previous research points one way, predict that direction. If not, predict only a difference or relationship.
4. Write the alternative hypothesis. One sentence, in the future tense, naming both conditions or both co-variables and the expected result.
5. Write the null hypothesis. The same sentence, rewritten to predict no difference or no relationship.

Worked example: writing a hypothesis from an exam scenario

Scenario: A psychologist has read that the wording of a question can affect what eyewitnesses report. She shows 60 students a video of a car accident. Half are asked “How fast were the cars going when they smashed into each other?” and half are asked the same question with “hit” instead of “smashed”. She records each student’s speed estimate in miles per hour.

The direction is justified by real evidence. In Loftus and Palmer’s (1974) first experiment, 45 students watched films of car accidents and were asked about speed using one of five verbs. Estimates were lowest for “contacted” (31.8 mph) and “hit” (34.0 mph) and highest for “smashed”. In their second experiment, 16 of the 50 students in the “smashed” group said they had seen broken glass, compared with 7 of the 50 in the “hit” group, although there was no broken glass in the film.

Notice what the finished hypothesis does. Someone who has never seen the study could read it and know exactly which two groups to compare, what to measure and in what units, and which result would support the prediction. That is the test of a good hypothesis.

Operationalising variables in a hypothesis

Operationalising a variable means defining it in a way that can be measured. Most variables psychologists care about, such as memory, stress, aggression or attachment, cannot be observed directly, so the hypothesis has to say what will be counted or scored instead. A hypothesis that is not operationalised is the most common reason for losing marks in exam questions on this topic.

Operationalising also affects what counts as a level of measurement. Words recalled out of 20 is a count, a speed estimate is on a scale with equal intervals, and a rating on a 10-point scale is ordinal. That in turn affects which statistical test the hypothesis will eventually be tested with, as our guide to levels of measurement explains.

Try it: hypothesis builder

Enter your own variables below and the builder writes the directional, non-directional and null hypotheses for you, in exam-ready wording. It also flags a dependent variable that does not look operationalised. Use it to check your own answers, not to replace them.

How to Write a Hypothesis for a Correlation

A hypothesis for a correlation predicts a relationship between two co-variables, rather than a difference between conditions. It names both co-variables, operationalises each one, and states whether the correlation will be positive, negative or simply present. There is no independent or dependent variable, because nothing is manipulated.

Two mistakes are especially common with correlational hypotheses. The first is writing it like an experiment: “people who revise more will get higher scores” sounds like a comparison of two groups, when the study measures one group on two variables. Use the word “correlation” or “relationship”. The second is implying cause. A correlational hypothesis predicts that two things vary together, not that one causes the other, so avoid words such as “affects”, “causes” or “leads to”. For the statistics that test these hypotheses, see our guides to the correlation coefficient and Spearman’s rho.

Hypothesis Examples in Psychology

The examples below show all three forms of hypothesis for the same study, across different areas of the A level course. Reading across a row shows how the wording changes; reading down a column shows the patterns that stay the same.

Each experimental hypothesis names two conditions of the independent variable and one operationalised dependent variable. How participants are allocated to those conditions, whether each person does both or only one, is a separate decision covered in our guide to experimental design. Who those participants are, and how they are chosen, is covered in sampling methods.

Do all studies need a hypothesis?

Not every study needs a hypothesis. Experiments and correlations, which test a prediction statistically, always have one. Exploratory and qualitative research often does not: a researcher interviewing young carers about their experiences, or carrying out a detailed case study, may start with a research question and an aim but no prediction. That is a strength of the approach rather than a weakness, because the point is to discover what matters to the participants instead of testing what the researcher already expects.

Common Mistakes When Writing Hypotheses

The most common mistakes when writing hypotheses are leaving the variables unoperationalised, writing a question or an aim instead of a prediction, and confusing a non-directional hypothesis with a null one. Each one costs marks in exam answers and each is easy to fix.

• Not operationalising the variables. “Revision improves memory” names the variables but does not say how either is measured. Say what is counted or scored.
• Writing a question. “Does caffeine affect reaction time?” is a research question. A hypothesis is a statement: “Participants who drink caffeine will…”.
• Writing an aim instead of a hypothesis. “To investigate…” is always an aim, never a hypothesis.
• Naming only one condition. “Participants who revise with music will recall fewer words” does not say fewer than whom. Name both conditions.
• Mixing up non-directional and null. “There will be a difference” is non-directional. “There will be no difference” is null.
• Using causal language for a correlation. Correlations predict a relationship, not an effect.
• Writing in the past tense. A hypothesis is a prediction, so it uses “will”.
• Saying the hypothesis was “proved”. Results support a hypothesis or fail to. They never prove it.

What Happens to a Hypothesis After the Study?

After the data are collected, the null hypothesis is tested with an inferential statistical test, chosen according to the design, the level of measurement and whether the study looks for a difference or a relationship. If the result is significant, the null is rejected and the alternative hypothesis is supported. If it is not, the null is retained. Our guide to choosing a statistical test walks through that decision.

HARKing: changing the hypothesis after the results

HARKing stands for Hypothesising After the Results are Known. The social psychologist Norbert Kerr (1998) defined it as presenting a hypothesis that was based on the results as if it had been made before the study began. Kerr presented survey data suggesting that some forms of HARKing were widely practised, even though they were also widely seen as inappropriate.

HARKing is a problem because a hypothesis written after seeing the data will always fit the data. It looks like a successful prediction when nothing was predicted at all. It also connects to confirmation bias: once a researcher sees an interesting pattern, it is natural to believe they expected it all along.

How often are psychology hypotheses supported?

Published psychology hypotheses are supported far more often than seems plausible. Fanelli (2010) examined 2,434 papers across the sciences that stated they had tested a hypothesis. Psychology and psychiatry had the highest rate of positive results of any field, at 91.5%, compared with 70.2% in space science. The odds of a positive result were about five times higher in psychology and psychiatry than in space science.

Registered Reports were designed partly to address this. In a Registered Report, the introduction, hypotheses and method are peer reviewed and accepted for publication before any data are collected, so the hypothesis cannot drift to fit the results and the paper is published whatever it finds. Scheel, Schijen and Lakens (2021) compared the first hypothesis in 152 standard psychology papers with the first hypothesis in 71 Registered Reports. It was supported in 96% of the standard papers but only 44% of the Registered Reports. The authors suggest that reduced publication bias, and fewer false positives, are plausible reasons for the gap.

The lesson for anyone writing a hypothesis is the one this guide started with. A hypothesis is only useful if it is written before the data, precisely enough that the data could prove it wrong.

Spot the Hypothesis: Practice Quiz

The quickest way to get confident is to classify real statements. Each of the ten below is an aim, a directional hypothesis, a non-directional hypothesis or a null hypothesis. Pick one and read the explanation.

Conclusion

An aim says what a study will investigate. A hypothesis predicts what it will find, in terms precise enough to be tested. The alternative hypothesis predicts a difference or relationship; the null hypothesis predicts none, and it is the null that statistical tests put on trial. The alternative can be directional, predicting which way the result will go when previous research justifies it, or non-directional when it does not.

The choice is more than wording. It decides whether a one-tailed or two-tailed critical value is used, and so can decide whether a result is significant. That is why hypotheses must be written, operationalised and fixed before any data are collected. Get that right, and every later step of a study, from choosing a statistical test to writing the discussion, has a clear question to answer.

Frequently Asked Questions

What is a hypothesis in psychology in simple terms?

In simple terms, a hypothesis is an educated prediction about what a study will show, written as a statement that the results could prove wrong. It is based on previous research or theory, it names what will be changed and what will be measured, and it is decided before the study is carried out.

What is an example of a directional hypothesis in psychology?

“Children who watch an adult behave aggressively towards a doll will copy more aggressive acts than children who watch a calm adult” is a directional hypothesis. It says which group will score higher, and it echoes Bandura, Ross and Ross (1961), whose predictions were based on earlier research into imitation.

What is a null hypothesis example in psychology?

“There will be no difference in reaction time, measured in milliseconds, between participants who drink coffee and participants who drink water” is a null hypothesis. It predicts that the independent variable will have no effect, so any difference in the results is put down to chance unless a statistical test shows otherwise.

When should you use a non-directional hypothesis?

Use a non-directional hypothesis when you cannot justify predicting a direction. That happens when a topic has little previous research, when earlier studies disagree, or when a result in either direction would be equally interesting. In exam scenarios, look for phrases such as “no previous research” or “mixed findings”.

Is an experimental hypothesis the same as an alternative hypothesis?

Yes, in an experiment they mean the same thing. “Experimental hypothesis” is simply the name given to the alternative hypothesis when the study is an experiment. For a correlation, observation or survey, the prediction is called the alternative or research hypothesis, because there is no experiment involved.

Is a one-tailed hypothesis the same as a directional hypothesis?

Yes. The two names are interchangeable, and a non-directional hypothesis is also called two-tailed. The “tails” are the two ends of a probability distribution: a one-tailed prediction looks for an extreme result at one end only, while a two-tailed prediction accepts an extreme result at either end.

How do you write a hypothesis for a correlation in psychology?

1. Name the two co-variables.
2. Operationalise both, saying how each is measured.
3. Decide whether you expect a positive or negative relationship, or cannot say.
4. Write: “There will be a [positive / negative] correlation between [co-variable 1] and [co-variable 2].”

What symbols are used for hypotheses?

H₀ stands for the null hypothesis and H₁ (sometimes H_a) for the alternative. In statistics, a directional hypothesis is shown with a greater-than or less-than sign between the two group means, and a non-directional one with a not-equal sign. A level psychology answers are normally written in words rather than symbols.

References

• AQA. (2015). AS and A-level Psychology specification (7181, 7182). AQA.
• Bandura, A., and Huston, A. C. (1961). Identification as a process of incidental learning. Journal of Abnormal and Social Psychology, 63(2), 311-318.
• Bandura, A., Ross, D., and Ross, S. A. (1961). Transmission of aggression through imitation of aggressive models. Journal of Abnormal and Social Psychology, 63(3), 575-582.
• Fanelli, D. (2010). “Positive” results increase down the hierarchy of the sciences. PLoS ONE, 5(4), e10068.
• Fisher, R. A. (1935). The design of experiments. Oliver and Boyd.
• Kerr, N. L. (1998). HARKing: Hypothesizing after the results are known. Personality and Social Psychology Review, 2(3), 196-217.
• Loftus, E. F., and Palmer, J. C. (1974). Reconstruction of automobile destruction: An example of the interaction between language and memory. Journal of Verbal Learning and Verbal Behavior, 13(5), 585-589.
• OCR. (2015). A Level Psychology H567 specification. OCR.
• Pearson Edexcel. (2015). Pearson Edexcel Level 3 Advanced GCE in Psychology (9PS0) specification. Pearson.
• Ruxton, G. D., and Neuhäuser, M. (2010). When should we use one-tailed hypothesis testing? Methods in Ecology and Evolution, 1(2), 114-117.
• Scheel, A. M., Schijen, M. R. M. J., and Lakens, D. (2021). An excess of positive results: Comparing the standard psychology literature with Registered Reports. Advances in Methods and Practices in Psychological Science, 4(2).

Further Reading and Research

Recommended Articles

• Statistical Significance: P-Values, Errors and Effect Sizes
• Choosing the Right Statistical Test: A Decision Guide
• T-Tests in Psychology: A Complete Guide

Suggested Books

• Coolican, H. (2019). Research Methods and Statistics in Psychology (7th ed.). Routledge.
  • The standard A level and undergraduate reference, covering aims, hypotheses, operationalisation and one-tailed and two-tailed testing with worked examples.
• Popper, K. (1959). The Logic of Scientific Discovery. Hutchinson.
  • The classic argument that scientific claims must be falsifiable, which explains why a hypothesis has to be capable of being proved wrong.
• Chambers, C. (2017). The Seven Deadly Sins of Psychology. Princeton University Press.
  • An accessible account of HARKing, publication bias and the reforms, including Registered Reports, designed to keep hypotheses honest.

Recommended Websites

• AQA A-level Psychology (7182)
  • The specification, past papers and mark schemes, including the research methods content on aims, hypotheses and operationalisation.
• PLoS ONE
  • Free full text of Fanelli (2010), with the rate of supported hypotheses for every scientific discipline studied.
• Center for Open Science
  • Explains how Registered Reports work and lists the journals that accept them.

Kathy Brodie

Kathy Brodie is an Early Years Professional, Trainer and Author of multiple books on Early Years Education and Child Development. She is the founder of Early Years TV and the Early Years Summit.

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To cite this article please use:

Early Years TV Aims and Hypotheses in Psychology: Directional vs Non-Directional. Available at: https://www.earlyyears.tv/hypotheses-psychology-aims-directional-non-directional/ (Accessed: 3 October 2026).