Causality concerns whether a relationship between variables represents cause-and-effect.

Psychologists investigate relationships between variables with the goal of determining whether one variable causes an effect in another.

Human behaviour is complex, so causality often reflects the interaction of several variables rather than one simple cause.

Some simpler relationships may nevertheless result from a direct causal relationship.

A strong causal explanation should therefore consider both direct effects and possible interacting influences.

Bidirectional ambiguity occurs when the direction of a relationship between variables is uncertain.

If two variables are related, the first may influence the second, or the second may influence the first.

The variables might also influence each other.

Without establishing direction, a researcher should be cautious about making a causal conclusion.

This is a key limitation when interpreting relationships between variables.

Internal validity concerns whether an observed effect can confidently be attributed to the proposed causal variable rather than other influences.

High internal validity strengthens a cause-and-effect conclusion.

External validity concerns how far findings can apply beyond the particular conditions in which they were obtained.

A study may provide strong evidence for causality under controlled conditions but have more limited external validity.

Both forms of validity should be considered when evaluating causal claims.

A placebo provides a comparison condition without the active element being investigated.

A double-blind procedure keeps both participants and relevant researchers unaware of condition allocation during the study.

These controls can reduce expectation-related influences that might otherwise provide alternative explanations.

A wait-list control delays an intervention for a comparison group while another group receives it.

These procedures can strengthen attempts to isolate a possible causal effect.

An influence means that a variable contributes to behaviour without necessarily being its sole cause.

An interaction occurs when the effect of one variable depends on or combines with another variable.

Recognizing interaction reflects the complexity of causal explanations in psychology.

Agency highlights the possible role of individual choice or action when explaining behaviour.

Motivation may also contribute to behaviour and should be considered where relevant to a causal explanation.

Correlation indicates that two variables are related or change together.

Causation means that one variable produces a change or effect in another.

A correlation alone does not establish which variable causes the other.

An observed relationship may also reflect other variables rather than a direct causal link.

In an exam, distinguish evidence of an association from evidence supporting a cause-and-effect relationship.

Reductionism explains behaviour by focusing on a limited number of variables or mechanisms.

A reductionist approach can make causal relationships easier to investigate by simplifying a complex behaviour.

However, human behaviour may involve several interacting variables, so an overly simple explanation may miss important influences.

Complexity therefore challenges claims that one variable alone completely causes a behaviour.

For evaluation, ask whether the proposed causal explanation adequately represents the complexity of behaviour.

Extraneous variables are variables other than the proposed causal variable that could influence the outcome.

If they are not controlled, they create alternative explanations for an observed effect.

Controls help researchers reduce these competing explanations and strengthen causal conclusions.

Effective control makes it easier to isolate the relationship being investigated.

When evaluating research, ask whether uncontrolled variables could plausibly account for the findings.

Statistical significance helps researchers judge whether an observed result is unlikely to have arisen through chance variation alone.

A statistically significant result can support evidence that variables are meaningfully related.

However, statistical significance alone does not demonstrate causality.

Causal conclusions still depend on the study design, controls, alternative explanations and validity.

Use statistical significance as one part of evaluating a causal claim, not as proof by itself.