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MENTAL MODEL · M5098

Regression Fallacy

Regression Fallacy
StructureHigh supportCritical Thinking
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Updated 2026-08-03

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INTRODUCTION

English translation pending.

CORE DEFINITION

The regression fallacy is the misuse of regression to the mean, the statistical fact that extreme observations tend to be followed by more average ones. When a bad extreme improves after a punishment, a treatment, or a redesign, the change is credited to the intervention when it may simply be natural drift back toward the typical. The corrective is methodological: without a comparison group, before-and-after improvement proves nothing, because the same improvement would likely have arrived on its own.

SCAFFOLDING EFFECT

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- Comparison requirement: Ask for the control group before accepting any before-and-after improvement. - Regression check: After an extreme event, estimate how much recovery the mean alone predicts. - Time-order guard: Distinguish what happened after the intervention from what happened because of it.

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Any measured quantity carries a mix of true level and random fluctuation. An extreme reading is extreme partly because the fluctuation pointed one way, and fluctuation does not persist, so the next reading is likely closer to the mean. Interventions applied at extremes therefore collect credit for recoveries that were already probable, and the more dramatic the initial deviation, the more dramatic the apparent cure.

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Source support: Explicit

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    en.wikipedia.orghttps://en.wikipedia.org/wiki/Regression_fallacyZH · Explicit
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