Correlation indicates a statistical relationship between two variables, suggesting they move together in some way, but it does not imply that changes in one cause changes in the other. Causation, on the other hand, implies a direct cause-and-effect relationship, where one variable directly influences the other, a distinction that is crucial for accurate data interpretation and decision-making.