Regression to the mean
Regression to the mean is the tendency for extreme results to be followed by more average ones, simply because luck evens out. An exceptional performance is usually part skill, part chance — and the chance part rarely repeats.
✦ Widely referenced — cross-referenced 18× across this reference (14 related ideas · 1 comparisons · 3 hubs) · The State of Thinking 2026 →
How it works
Most outcomes mix skill and luck. When you observe an extreme, an unusual dose of luck is often involved; next time luck reverts to typical, so the result drifts back toward the average — with no other cause needed.
Half of what looks like cause is just luck reverting to the average.
How to use it
- Don’t over-react to a single extreme result, good or bad — expect it to moderate.
- Beware crediting an intervention for improvement that was just regression.
- Judge ability over many trials, not one outlier.
Worked example
A rookie’s record-breaking first season is often followed by a weaker second — the “sophomore slump” — not because praise spoiled them, but because their peak mixed skill with luck that reverted. The same logic explains why punishing a bad result often “seems to work.”
Where it fails
It’s easy to mistake regression for cause — to credit a coach, a drug, or a scolding for improvement that would have happened anyway. Always ask whether an extreme baseline made reversion inevitable.
- It presumes a stable underlying mean — if the system itself changed (new skill, new regime, new drug that works), reversion is not owed and 'expecting regression' becomes denial of real change.
- The model says extremes revert but not by how much or when; without knowing the luck-versus-skill split, the prediction is directional at best.
- In systems with feedback, extremes can self-reinforce instead of reverting — a bank run or viral hit moves further from the mean because it was extreme.
The counter-model: Compounding — Regression pulls extreme results back toward the average; compounding lets early advantages feed themselves and diverge — diagnosing which force governs your situation decides whether an outlier will fade or snowball.
How to apply it, step by step
- When you see an extreme result — great or terrible — write down the performer's longer baseline.
- Estimate how much of the extreme was chance: small samples and volatile domains mean more luck.
- Predict the next result closer to the baseline than to the extreme.
- Before crediting an intervention with improvement, ask whether reversion alone predicts it.
- Make the decision — hire, invest, punish, reward — on the baseline, not the outlier.
The deeper point
Half of what looks like cause is just luck reverting. Most management "wins" — punishing the slump, praising the streak — are quietly taking credit for regression, which is why so much advice "works" and so little replicates.
Frequently asked
- What is regression to the mean?
- The tendency for extreme outcomes to be followed by more average ones, because the luck component of an extreme result rarely repeats.
- Why does regression to the mean fool people?
- It makes useless interventions look effective — after a terrible result things improve on their own, and whatever you did gets the credit.
- Is regression to the mean a real cause?
- No — it’s a statistical artifact of mixing skill and luck. No mechanism is needed; extremes simply tend to be followed by less-extreme values.
Biases this model helps counter
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Cite this page
ReadGlobe. (2026). Regression to the mean. https://readglobe.com/model/regression-to-the-mean/
"Regression to the mean." ReadGlobe, 29 May 2026, readglobe.com/model/regression-to-the-mean/.
Primary source: Wikipedia
Editorial synthesis © ReadGlobe 2026, drawing on the mental-models tradition (Charlie Munger, Farnam Street) and the primary sources for each model. · Last reviewed 2026-05-29.