Why a strong statistical relationship between two variables isn't proof that one causes the other.
Every data science course drills the same warning: "correlation doesn't imply causation." Most people can recite it, and most people still get fooled by it, because a high correlation coefficient feelslike evidence of a mechanism. It isn't. Correlation is a purely statistical, observational fact — it tells you two variables tend to move together. Causation is a claim about mechanism — that a change in one variable actually produces the change in the other. Confusing the two is one of the most common and most consequential errors in applied data science.