Insensitivity to Sample Size
Updated 2026-08-02
INTRODUCTION
English translation pending.
CORE DEFINITION
Insensitivity to sample size is the tendency to judge the reliability of a result without regard to how many observations support it. Asked about two hospitals, most people say a small hospital and a large hospital are equally likely to record days when sixty percent of births are boys, when the smaller hospital is in fact far more likely to show such extremes. Key qualification: the bias concerns how evidence is evaluated, and it interacts with the representativeness heuristic.
SCAFFOLDING EFFECT
Reduce cognitive load
- Count the observations: state the sample size before interpreting any pattern. - Expect extremes: assume small samples will show large deviations purely by chance. - Widen the interval: express conclusions from small samples as ranges rather than point estimates.
Anchor fast decisions
Small samples vary more because random fluctuations are not averaged out, so extreme results appear far more often than they do in large samples. People ignore this because a small sample looks like a miniature version of the population, which makes its proportions feel equally informative. The result is overconfidence in patterns that are mostly noise.
MINIMUM ACTION
In progress 0/1Practice this model in one real situation:
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Source support: Explicit
- en.wikipedia.orghttps://en.wikipedia.org/wiki/Insensitivity_to_sample_sizeverified
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