Which statement best describes correlation between two variables?

Which statement best describes correlation between two variables?

• A. A correlation is a statistical relationship between two variables; it does not imply causation.
• B. Correlation proves causation.
• C. Correlation is the difference between means.
• D. Correlation is a measure of reliability.

Answer: (A)

Correlation describes a statistical relationship between two variables; it shows how one tends to vary with the other, but it does not prove that one causes the other. A correlation can be positive, negative, or zero, and its strength indicates how closely the variables move together, without indicating which causes which or whether a third factor is involved. This is why the statement is correct: it captures that association without implying causation, which would require experimental evidence or controlling for other factors. The other ideas—causation being proven by correlation, correlation being about differences between means, or correlation measuring reliability—don't fit, because correlation is about the relationship between two variables, not about proving cause, comparing group means, or assessing consistency of a measure.

Correlation describes a statistical relationship between two variables; it shows how one tends to vary with the other, but it does not prove that one causes the other. A correlation can be positive, negative, or zero, and its strength indicates how closely the variables move together, without indicating which causes which or whether a third factor is involved. This is why the statement is correct: it captures that association without implying causation, which would require experimental evidence or controlling for other factors. The other ideas—causation being proven by correlation, correlation being about differences between means, or correlation measuring reliability—don't fit, because correlation is about the relationship between two variables, not about proving cause, comparing group means, or assessing consistency of a measure.