Expect extreme results to regress toward average
Unusually good or bad performance predicts more average performance next time — account for this before giving praise or blame.
Why it works
Regression to the mean is a mathematical inevitability whenever performance involves both skill and chance. The representativeness heuristic causes people to ignore it: they explain extreme outcomes as if they fully reflect the underlying reality ("they’ve turned a corner" / "this person is hopeless"), rather than partly reflecting chance fluctuation. This produces the famous intervention illusion: harsh feedback after a bad performance "works" and praise after a good one "hurts" — simply because extreme outcomes regress, regardless of the intervention.
How to do it
- After observing an unusually high or low result, ask: "How much of this could be due to chance fluctuation around the true average?"
- Predict that the next result will likely be closer to the person’s or process’s long-run average, not as extreme.
- Before attributing a change to an intervention, verify it with more than one or two follow-up observations.
- Separate feedback on what you can observe from conclusions about underlying talent or state.
Evidence
Regression to the mean is a mathematical property of correlated variables (Francis Galton identified it in 1886). Kahneman’s intervention illusion example (flight instructors praising vs. criticizing) illustrates how ignoring regression causes false causal attributions; this is a teaching example, not a formal study. (mechanistic)
How much any result regresses depends on how strongly skill vs. luck determines it; regression matters more in high-variance, luck-influenced domains than in highly skill-determined ones.
Sources
- Galton (1886), Regression towards mediocrity in hereditary stature, Journal of the Anthropological Institute of Great Britain and Ireland
Common mistake
Treating regression as a reason to ignore feedback — real skill improvements happen, but they should be confirmed over multiple observations, not inferred from a single extreme outcome.
Practice this with IX Coach
7 days free, then $40/month (~$1.30/day).
More practices for The Representativeness Heuristic — Judging by Resemblance
- Look up base rates before forming a resemblance judgment
Before deciding "this looks like X," ask how common X actually is in the relevant population.
- Identify which features are actually diagnostic
Separate features that genuinely differentiate categories from ones that just complete the picture.
- Recognize that random sequences don’t "owe" balance
Random processes have no memory — a run of heads doesn’t make tails more likely.
- Treat small samples with explicit skepticism
A short sequence can look representative without being statistically reliable — adjust confidence for sample size.
- Audit person-judgments for prototype substitution
Check whether a judgment about a person is based on their actual behavior or on resemblance to a type.
- Name the prototype you’re comparing against before deciding
Make the prototype you’re using as a reference explicit — hidden templates bias decisions without scrutiny.