Expect regression to the mean in extreme outcomes

Unusually good or bad performance tends to be followed by more average performance — not because of what you did.

Why it works

Regression to the mean is a mathematical property of any measured variable with random components: extreme high values are likely to be followed by lower values, and vice versa, simply because the extreme reading partly reflected a lucky draw. People systematically misattribute this regression to their own actions (the coach whose harsh criticism after a bad game appears to produce improvement) — Kahneman’s classic example of a mechanism that creates false causal beliefs.

How to do it

1. When you observe an extreme performance (very good or very bad), ask: "How much of this might be random variation?"
2. Resist attributing the subsequent regression to your intervention unless you have a controlled comparison.
3. Use averages over time rather than single extreme readings as your benchmark.

Evidence

Regression to the mean is mathematically derived and documented in a wide range of applied settings from athletic performance to medical symptom progression. Kahneman documented the misattribution of regression effects in flight instructor training and elsewhere. (observational)

Sources

• Kahneman (2011), Thinking, Fast and Slow — regression to the mean and misattribution chapter

Common mistake

Concluding that a harsh response "worked" because performance improved after it — without controlling for the regression that would have occurred anyway.