Correlation vs causation

Correlation means two variables move together, either in the same direction (both increase or decrease) or in opposite directions (one goes up while the other goes down). Causation means one directly causes the other. Ice cream sales correlate with drowning deaths because both increase in summer, but ice cream doesn't cause drowning. This distinction prevents costly product mistakes based on false assumptions.

Product metrics often show misleading correlations. Users who engage with premium features have higher retention, but forcing all users into premium features won't magically improve retention. These power users were already more engaged. The feature usage reflects their commitment, not the cause of it.

Testing causation requires controlled experiments. A/B tests isolate variables to establish true cause-and-effect relationships.^[1] Time delays matter too. For example, a marketing campaign might show immediate traffic spikes but revenue impact appears weeks later. Always question whether correlation implies causation before making product decisions.