What is the difference between correlation and causation?

The distinction between correlation and causation is crucial in understanding data relationships. Correlation describes a statistical association between two variables, meaning that when one variable changes, the other tends to change as well. However, this change does not imply that one variable causes the change in the other; they may simply be related due to another underlying factor or coincidence.

Causation, on the other hand, denotes a direct cause-and-effect relationship. If one variable causes another, it means changes in the first variable will directly result in changes in the second variable. A clear example would be how increased temperature (the cause) directly results in the melting of ice (the effect).

Understanding this difference is vital in fields such as statistics and research, where attributing causality without proper evidence can lead to incorrect conclusions. Thus, stating that correlation implies a relationship while causation indicates direct influence accurately captures the fundamental distinction between the two concepts.