Biased evaluations emerge from inferring hidden causes

How do we evaluate a group of people after a few negative experiences with some members but mostly positive experiences otherwise? How do rare experiences influence our overall impression? We show that rare events may be overweighted due to normative inference of the hidden causes that are believed to generate the observed events. We propose a Bayesian inference model that organizes environmental statistics by combining similar events and separating outlying observations. Relying on the model’s inferred latent causes for group evaluation overweighs rare or variable events. We tested the model’s predictions in eight experiments where subjects observed a sequence of social or non-social behaviors and estimated their average. As predicted, estimates were biased toward sparse events when estimating after seeing all observations, but not when tracking a summary value as observations accrued. Our results suggest that biases in evaluation may arise from inferring the hidden causes of group members’ behaviors.