Correlation vs. Causation

Data reveals relationships, not causes. Two phenomena may occur together without one causing the other. Yet visualization often tempts causal conclusions.

This confusion is dangerous because it drives misguided action. Teams optimize levers that are merely side effects, while the real drivers remain untouched.

Mature analysis separates observation from explanation. It uses experiments, counterfactuals, and qualitative insight to test hypotheses. Causation is not assumed—it is earned.