Correlation vs. Causation: Four Ways Data Analysts Get It Wrong
The phrase 'correlation doesn't imply causation' is widely repeated in data science but rarely explained with clarity. Correlation simply describes how two variables move together, measured by a coefficient, while causation means one variable directly produces a change in another. Confusion arises through four common traps: confounding variables, where a hidden third factor drives both observed variables; reverse causality, where the presumed effect is actually driving the cause; spurious correlations, which are coincidental patterns with no real link; and selection bias, where unrepresentative data creates misleading relationships. Distinguishing the two concepts requires outside knowledge, controlled testing, or careful examination of timing and data collection methods.
