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Sampling-based models, which assume that people remember or simulate instances of events and count outcome propor-tions to make probability judgments, account for many apparent biases in human judgment. The success of such modelsis generally dependent on sample size, as particularly large or particularly small samples are often required for a modelto reproduce effects observed in data. Thus, systematically exploring the actual number of instances that people tend tosample is an important criterion in evaluating model credibility. Here we propose a method of estimating sample size byway of inter-judgment variance. We show through model recovery that this method will reliably recover the correct samplesize and subsequently apply the method to two well-supported models of human probability judgment, Probability TheoryPlus Noise and the Bayesian Sampler. Results indicate, in both cases, that human probability judgments are based on arelatively plausible (¡ 10) number of sampled instances.
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