Base Rate Fallacy
Updated 2026-08-06
INTRODUCTION
English translation pending.
CORE DEFINITION
The base rate fallacy is the tendency to overweight specific, vivid evidence and underweight the prior probability that the event occurs at all. If a disease is very rare, a positive result on an accurate test still leaves a low probability of actually having it, because the small number of true cases is swamped by false positives among the many healthy people. Bayesian updating combines the base rate with the evidence to produce the correct posterior.
SCAFFOLDING EFFECT
Reduce cognitive load
- Start with the prior: establish how common the event is before considering the specific evidence. - Combine, do not replace: use Bayesian updating so the evidence adjusts the prior rather than overwriting it. - Discount anecdotes: treat striking individual cases as noise against the statistical signal.
Anchor fast decisions
Judgment is drawn to concrete, individuating information because it is easier to imagine and feels more diagnostic than an abstract frequency. When the base rate is very low, even an accurate test produces many more false positives than true positives, so the specific result carries less information than it appears to. Combining the prior with the likelihood ratio yields the posterior probability.
MINIMUM ACTION
In progress 0/1Practice this model in one real situation:
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Source support: Explicit
- zh.wikipedia.orghttps://zh.wikipedia.org/wiki/%E5%9F%BA%E6%9C%AC%E6%AF%94%E7%8E%87%E8%AC%AC%E8%AA%A4verified
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