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MENTAL MODEL · M2008

Bonferroni Correction

Bonferroni Correction
TechnicalHigh supportStatistics
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Version 1.0.0 · Updated 2026-07-30

CORE DEFINITION

When conducting multiple statistical tests, the probability of false positives (false alarms) accumulates with the number of tests. To maintain overall rigor, the significance threshold for each individual test must be greatly increased (e.g., dividing the P-value threshold by the number of tests).

SCAFFOLDING EFFECT

psychology

Reduce cognitive load

A tool for debunking false claims in big data mining. Why are many 'scientific discoveries' or 'marketing data' later proven false? Because if you search for correlations in massive data, as long as you try enough times, you will always find a statistical correlation like 'eating jelly causes acne' (pure coincidence). Data does not lie, but the people interrogating the data do. Uncorrected multiple hypotheses.

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Anchor fast decisions

In multiple comparisons, the more tests you run, the more likely you are to get false positives; the Bonferroni correction divides the significance threshold by the number of comparisons to control the family-wise error rate.

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Source support: Explicit

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