What is the difference between correlation and causation?

The distinction between correlation and causation is fundamental in data-driven decision-making and statistical analysis. Correlation refers to a statistical relationship between two variables, where changes in one variable are associated with changes in another. This relationship can be positive, negative, or non-existent, but it does not imply that changes in one variable are the result of changes in the other.

Causation, on the other hand, indicates that one variable directly affects another. When a causal relationship exists, changes in the cause lead to changes in the effect, making it a more definitive claim than correlation.

Understanding this difference is crucial because assuming causation from correlation can lead to inaccurate conclusions and poor decision-making. For example, if a study finds that ice cream sales correlate with an increase in drowning incidents, one cannot conclude that ice cream sales cause drowning; rather, there is a third variable (like warmer weather) that influences both.

Therefore, the choice that states correlation indicates a relationship while causation implies a direct effect captures this important distinction accurately.