What Is the Difference Between Correlation and Causation?
Correlation describes a relationship in which two variables tend to change together. When one rises, the other tends to rise, or fall, in a regular pattern. Causation means that a change in one variable directly produces a change in the other. The two are easy to confuse, and mistaking correlation for causation is one of the most common errors in reasoning about data.
Correlation
Correlation is measured by statistics, such as the correlation coefficient, which ranges from -1 to +1. A positive value means that the variables rise together, a negative one that as one rises the other falls, and a value near zero suggests no linear relationship. Correlation shows association only.
Why correlation is not causation
There are several reasons why variables may be correlated without one causing the other:
- Coincidence: with enough variables, some will line up by chance.
- A third variable: a hidden factor affects both. Ice cream sales and drowning deaths rise in summer because of hot weather, not because ice cream causes drowning.
- Reverse causation: the cause may run the other way. Do people who exercise feel happier, or do happier people exercise more?
- Selection effects: the groups compared may differ in other ways.
Evidence for causation
Researchers use several approaches to test causal claims.
- Randomized controlled trials: randomly assigning participants to treatment and control groups so that other factors balance out.
- Natural experiments and quasi-experiments: using situations in which circumstances create comparison groups.
- Longitudinal studies: following people over time to see which change comes first.
- Statistical controls: adjusting for other variables, although unmeasured factors may remain.
- Mechanisms and consistency: a plausible explanation, supported by multiple studies in different contexts.
Established criteria, such as temporal order, dose-response relationships and consistency, can help assess causal claims in observational research.
Spotting the error in the news
Headlines often imply cause from correlation, such as "Coffee drinkers live longer." Ask:
- Was it an experiment or an observation?
- Could another factor explain both?
- Could the cause run in reverse?
- How large and consistent is the effect?
- Do the authors claim cause or only association?
In your writing
Use careful language. Say "is associated with" or "is linked to" for correlations, and reserve "causes" or "leads to" for well-supported causal claims. Discuss limitations and alternative explanations.
Why it matters
Understanding the difference helps you read research critically, design studies and avoid drawing false conclusions that may affect decisions in health, policy and everyday life.
Related terms
See variable, statistical significance, peer review, IRB and gray literature.
Frequently asked questions
Does correlation imply causation?
No. Two variables can move together because of chance, a third factor or the direction of the relationship, and not because one causes the other.
How can researchers show causation?
Best through randomized experiments, which control for other factors. Strong observational evidence can also support causal claims when designs are careful and results are consistent.
What is a confounding variable?
A factor that influences both the supposed cause and the effect, creating a misleading link between them.