regression-to-the-mean
Regression to the Mean
Overview
Regression to the mean is the statistical regularity that any noisy measurement producing an extreme value tends to be followed on retest by a less-extreme value — because the extreme portion was partly driven by non-repeating random noise. There is no "force pulling back to average"; it is a mathematical consequence of signal + noise structure.
Named by Francis Galton (1886) studying parent-child height: tall parents have tall children, but slightly shorter; short parents have short children, but slightly taller. Kahneman's Israeli Air Force example (2011, Ch. 17) is the most-cited operational case — flight instructors concluded punishment works and praise doesn't, but were observing regression, not causation.
Composes with survivorship-bias (extreme survivors regress), probabilistic-thinking (regression is probabilistic), narrative-fallacy (regression drives post-hoc narratives), fundamental-attribution-error (attributing regression to character/intervention is FAE).