Which statement correctly distinguishes causation from correlation?

• A. Correlation implies causation.
• B. Causation is a direct cause-and-effect relationship.
• C. Correlation is a statistical association; causation is a direct cause-and-effect relationship.
• D. They are unrelated concepts.

Answer: (C)

Think of correlation as a pattern where two variables move together in some way. It’s a statistical association: when one variable changes, the other tends to change in a predictable direction, but this pattern doesn’t explain why it happens. Causation means a direct cause-and-effect link: one thing actually brings about a change in the other, usually shown by the cause preceding the effect and supported by evidence that rules out other explanations.

That’s why the statement that distinguishes them is best: correlation is a statistical association, while causation is a direct cause-and-effect relationship. It clearly separates the idea of simply observing a relationship (association) from the idea of one thing producing the change in another (causation).

Why not the other ideas? Because seeing two things move together does not prove one causes the other, so correlation does not imply causation. And saying they’re unrelated ignores that correlations can exist even when there isn’t a causal link. While causation involves a direct effect, it’s not enough to know that both exist in the abstract; you need a genuine causal connection, not just an association.