A sampling approach to biases in conditional probability judgments: beyond base rate neglect and statistical format.
Conditional probability judgments of rare events are often inflated. Early accounts assumed a general deficit in using statistical base rates. More recent approaches predict improvement when problems are presented in frequency format or refer to natural categories. The present theory focuses on sampling processes. Experiment 1 showed that a seeming advantage of frequency over probability formats is due to a confounded factor, the need to mentally transform stimulus samples. An information search paradigm was used in Experiment 2. When sampling by the predictor, the probability to be estimated, p(criterion/predictor), was conserved in the samples and judgments were quite accurate. However, when sampling by the criterion, the low base-rate event was strongly overrepresented, accounting for the entire bias. Judgments were quite sensitive to the sampled data, but failed to take sampling constraints into account, as shown in Experiments 3 and 4.