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

The alternative hypothesis predicts a specific effect, difference, or relationship in the data. In contrast, the null hypothesis predicts that no such effect or relationship exists and that any observed patterns are due to chance.

When should a researcher use the term 'correlation' instead of 'difference' in a hypothesis?

A researcher uses 'correlation' when investigating the relationship between two co-variables measured on the same participants. 'Difference' is used when comparing the performance or behavior of two or more distinct groups or conditions.

How does the Independent Variable (IV) differ from the Dependent Variable (DV)?

The IV is the variable that the researcher deliberately manipulates or changes to observe an effect. The DV is the variable that is measured to see how it responds to those changes; it is the 'effect' in the cause-and-effect relationship.

What is a common error when defining the Dependent Variable (DV) in a hypothesis?

A common error is failing to operationalise the DV, leaving it as a vague concept (e.g., 'memory') rather than a measurable quantity (e.g., 'number of items recalled'). The DV must always be quantitative to allow for objective analysis.

What happens if a researcher writes a hypothesis that is not falsifiable?

If a hypothesis cannot be proven wrong, it is not scientifically valid. Falsifiability ensures that the statement can be tested against empirical data, allowing the scientific community to accept or reject the claim based on evidence.

Why is it a mistake to write a hypothesis after the data has already been collected?

Hypotheses must be formulated 'a priori' (before the fact) to prevent researcher bias. Writing them after seeing the results leads to 'post-hoc' reasoning, where the prediction is tailored to fit the data rather than testing a genuine theory.

Define 'Operationalisation' in the context of psychological research.

Operationalisation is the process of defining variables in specific, observable, and measurable terms. This ensures that the research is clear, objective, and can be exactly replicated by other scientists.

What is a 'Co-variable'?

A co-variable is one of the two variables investigated in a correlational study. Unlike experimental variables (IV/DV), co-variables are simply measured to see if they change together in a predictable way.

What does it mean to 'reject the null hypothesis'?

Rejecting the null hypothesis means that the research findings are statistically significant, suggesting that the observed effect or relationship is unlikely to have occurred by chance and instead supports the alternative hypothesis.

Why must a hypothesis be a statement rather than a question?

A hypothesis must be a statement because it is a specific prediction of an outcome. A question (e.g., 'Does light affect growth?') does not provide a clear direction for testing, whereas a statement (e.g., 'Increased light will increase growth') does.

Formulation of Testable Hypotheses | OCR GCSE Psychology

Formulation of Testable Hypotheses

Summary

A testable hypothesis is a precise, predictive statement that outlines the expected relationship between variables in a scientific study. It serves as the foundation for experimental design by defining what is being manipulated and what is being measured, allowing researchers to either support or refute their predictions through empirical data.

1. Definition & Core Concepts

A hypothesis is a formal, testable statement that predicts the outcome of a research study before data collection begins. It acts as a bridge between theoretical ideas and empirical observation.
To be scientifically valid, a hypothesis must be falsifiable, meaning it is structured in a way that allows it to be proven wrong if the data does not support the prediction.
Every research study requires two distinct types of hypotheses: the alternative hypothesis and the null hypothesis, which together cover all possible outcomes of the investigation.

2. The Alternative Hypothesis ($H_a$)

The alternative hypothesis predicts that a specific pattern, relationship, or effect will be found in the results of the study.
It can take two primary forms: a difference (comparing two or more groups, such as an experimental group vs. a control group) or a correlation (examining the relationship between two co-variables).
A well-constructed alternative hypothesis must explicitly mention both the Independent Variable (IV) and the Dependent Variable (DV) to ensure the prediction is clear and focused.

3. The Null Hypothesis ($H_0$)

The null hypothesis is a statement of 'no effect' or 'no relationship,' predicting that any observed patterns in the data are due to chance rather than the variables being studied.
It serves as the baseline for statistical testing; researchers aim to gather enough evidence to reject the null hypothesis in favor of the alternative hypothesis.
For example, if an alternative hypothesis predicts that a new teaching method will improve scores, the null hypothesis would state that there will be no difference in scores regardless of the teaching method used.

4. Operationalisation of Variables

Diagram showing the relationship between the Independent Variable (the cause) and the Dependent Variable (the effect), linked by the hypothesized relationship and the requirement for operationalisation.

5. Key Distinctions: Difference vs. Correlation

6. Exam Strategy & Tips