A change in one variable coincides with a change in another variable but does not necessarily mean causation is present. This is called?

• A. Correlation
• B. Causation
• C. Hypothesis
• D. Empirical Evidence

Answer: (A)

The main idea here is recognizing a relationship where two variables move together without proving one causes the other. This is correlation. It describes an association—when one variable changes, the other tends to change in a predictable way, but that doesn’t show that one thing makes the other happen. Correlations can be positive (they move in the same direction) or negative (they move in opposite directions). A key caution is that a third factor could influence both, or the apparent link could be coincidental, or the direction of influence could be reversed.

Causation would mean one variable directly brings about a change in the other, which is a stronger claim than simple association. A hypothesis is a testable statement about a proposed relationship to be examined with data. Empirical evidence refers to the data and observations gathered to support or challenge a claim.