Overview
Regression to the mean is the statistical tendency for extreme measurements — very high or very low — to be followed by ones closer to the average, whenever chance plays a role. An exceptional result is often part skill and part luck; the luck rarely repeats, so the next result drifts back toward normal.
The model prevents a classic error: mistaking natural regression for cause and effect. A slump that ends, or a star performer who 'declines', may simply be reverting to their true average.
When to use it
Interpreting extreme results that involve luck — performance, testing, streaks.
How to apply it
Spot the extreme result
Notice an unusually high or low outcome.
Ask how much was luck
Consider the role of chance versus stable skill in that result.
Expect regression
Anticipate the next result moving back toward the average.
Don't confuse it with causation
Avoid crediting an intervention for a change that was just regression.
Common pitfalls
- Crediting a coaching change or new tactic for improvement that was just regression.
- Punishing a below-average result and thinking the punishment caused the rebound.
- Extrapolating an extreme result as if it were the new normal.
Frequently asked questions
Why does regression to the mean happen?
Because extreme results usually combine skill and luck; the luck doesn't reliably repeat, so subsequent results drift toward the true average.
What error does it cause?
Attributing a return-to-normal to some intervention — crediting a fix, or blaming a decline — when it was just statistical regression.