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).
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 structure keeps research intentions explicit and results comparable across studies.
Non-Directional Hypothesis
A non-directional hypothesis, or two-tailed hypothesis, predicts a difference between two variables without specifying its direction, or which group will score higher.
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 direction of the independent variable’s effect on the dependent variable, not just that a difference exists. For example, it might state that scores will be greater, smaller, or otherwise different in a specified way.
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 holds that for a theory or hypothesis to count as scientific, it must be testable and refutable.
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.
A prediction is never derived from a hypothesis alone. Testing it also depends on auxiliary assumptions about the equipment, the measures and the sample.
When a prediction fails, logic alone cannot say whether the hypothesis is wrong or one of these assumptions is. This is sometimes called the Duhem–Quine problem: a single disconfirmed prediction rarely refutes a hypothesis outright.
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.
Contemporary Research
This limit on what a single result can show has been demonstrated directly.
Open Science Collaboration (2015) repeated 100 published psychology studies using high-powered, pre-registered protocols.
Ninety-seven per cent of the original studies had reported a significant result. Only 36% of the replications did. Half the original effect vanished on average.
Simmons, Nelson and Simonsohn (2011) showed how easily a single study can mislead.
They used ordinary, widely tolerated flexibility in data analysis, such as trying more than one outcome measure or adding a few participants before checking again. The false-positive rate for a genuinely absent effect rose sharply. It reached above 60% when the four tricks were combined.
The recommended safeguard is to specify the hypothesis and the analysis plan before data collection. Preregistering a study this way keeps the distinction between generating a hypothesis and testing it verifiable, which is what gives the resulting p-value its meaning.
How to Write a Hypothesis
- Identify the Variables. Establish which variable the researcher manipulates (the independent variable) and which is the measured outcome (the dependent variable).
- Operationalise the Variables. Make the variables physically measurable or testable, e.g. if you are studying aggression, you might count the number of punches thrown.
- 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 the findings in the literature are limited or ambiguous, write a non-directional (two-tailed) hypothesis.
- Make It Testable. Ensure your hypothesis can be tested through experimentation or observation, and that it is possible in principle to prove it false (the principle of falsifiability).
- Use Clear and Concise Language. A strong hypothesis is short, typically one or two sentences, and phrased in straightforward terms, so it is easily understood and unambiguously testable.
- Pair It With an Explicit Null. State the null hypothesis alongside the alternative, so a reader can see exactly what claim the significance test is evaluating.
- Make It Precise Enough to Replicate. A well-written hypothesis lets an independent researcher, given only the hypothesis and the raw data, run the same analysis and reach the same conclusion.
Testing a Hypothesis: Significance, P-Values and Error Types
Once a hypothesis is written, a study collects data and puts it through a significance test. Understanding what this test actually shows, and what it does not, is essential to reading a result correctly.
What a Significance Test Actually Shows
A significance test does not examine the alternative hypothesis (H1) directly. It asks a narrower question.
It calculates how surprising the observed data would be if the null hypothesis (H0) were true. The logic runs backwards from the data. If the data would be very unlikely under H0, the researcher rejects the null and treats the result as support for H1.
The conventional cut-off is a p-value below .05. This means the researcher accepts up to a 5% chance of a false positive if H0 is actually true.
A smaller p-value does not mean a bigger or more important effect. Size and significance are different things. It depends on both the size of the effect and the sample size. A large enough study can turn a trivial difference into a very small p-value.
Type I and Type II Errors
Because the decision is made under uncertainty, two mistakes are possible when a result comes in. A Type I error is a false positive.
It means rejecting a true null hypothesis and concluding an effect exists when it does not. A Type II error is the opposite mistake. It is a false negative: failing to reject a false null hypothesis and missing a real effect.
Statistical power is the probability that a study detects a genuine effect of a given size. Low power raises the risk of a Type II error.
It also inflates the effect size of any result that clears the significance bar. Only unusually large estimates pass the threshold in an underpowered study. A single small trial rarely tells the whole story.
Choosing a One-Tailed or Two-Tailed Test
The form of the hypothesis decides how the significance level is spread across the sampling distribution. This choice matters.
A two-tailed test, used for a non-directional hypothesis, splits the chosen alpha between both tails. A result in either direction counts as evidence against the null. The choice is a trade-off.
A one-tailed test, used for a directional hypothesis, places the whole 5% in a single tail. This makes it easier to reach significance in the predicted direction.
A large effect in the unexpected direction is invisible to a one-tailed test. Size does not matter here.
The direction has to be chosen and recorded before the data are seen. Switching to a one-tailed test after seeing the results inflates the real error rate. This is hypothesising after the fact.
Examples
Consider a hypothesis many teachers might subscribe to: students work better on Monday morning than on Friday afternoon (IV=Day, DV=Standard of work).
Suppose the study gives the same students a lesson on Monday morning and again on Friday afternoon, then tests recall of each session’s material. The two hypotheses would read:
- 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.
Key Takeaways
- H1 vs H0: The alternative hypothesis (H1) predicts a relationship or difference; the null hypothesis (H0) predicts none. A significance test evaluates H0, not H1, directly.
- Directional vs Non-Directional: A directional (one-tailed) hypothesis predicts which way an effect will go. A non-directional (two-tailed) hypothesis predicts only that a difference exists.
- Operationalisation: Both variables in a hypothesis must be defined in measurable terms before data collection, or the hypothesis cannot be tested.
- Falsifiability: A scientific hypothesis must be capable of being shown false by some possible evidence, not just capable of being confirmed.
- Never Proven: A study can support or fail to support a hypothesis. Statistics never prove one true.
- Replication: Landmark replication studies now find that a single significant result holds up only about a third of the time, so one study rarely settles a question.
References
Open Science Collaboration. (2015). Estimating the reproducibility of psychological science. Science, 349(6251), aac4716. https://doi.org/10.1126/science.aac4716
Simmons, J. P., Nelson, L. D., & Simonsohn, U. (2011). False-positive psychology: Undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychological Science, 22(11), 1359–1366. https://doi.org/10.1177/0956797611417632
