Which statement reflects the relationship between correlation and causation?

• A. Correlation implies causation.
• B. Correlation does not imply causation; causation requires evidence of a causal mechanism.
• C. Causation can be inferred from a strong correlation alone.
• D. Two variables with correlation have no relationship.

Answer: (B)

Understanding correlation versus causation means recognizing that two things can move together without one causing the other. A strong relationship between two variables can show they are linked in some way, but it doesn’t prove that changing one will cause a change in the other.

To claim causation, you need evidence that manipulating the cause leads to a change in the effect, and you should rule out other explanations such as a third factor driving both variables. This often comes from well-designed experiments or analyses that establish temporal order and control for confounding factors. For example, if you run a randomized trial where you change a treatment and observe that the outcome changes accordingly, you’re building a case for causation rather than mere association.

The statement that best captures this idea is that correlation does not imply causation; causation requires evidence of a causal mechanism. The other ideas—claiming correlation proves causation, or that a strong correlation alone is enough to infer causation, or that correlated variables have no relationship—misinterpret how links between variables should be interpreted because they ignore the possibility of confounding factors, coincidental patterns, or the need for demonstration of a causal pathway.