Environmental restrictions often permit the sampling of some items while excluding others. Such restrictions are termed sampling frames, whereby items can be selected based on their category membership (category frame), or possession of a target property (property frame). According to Bayesian principles, narrower property generalization is expected when a sample is subject to property sampling than category sampling. The current work examined whether sensitivity to such sampling frames could be increased through training with worked examples and practice. Experiment 1 found that training in property or category sampling enhanced sensitivity to that frame relative to a no-training control. Experiment 2 employed a pre-post design where all participants received training in both frames. A positive training effect was found, but only for those with a poor understanding of sampling frames on the pre-test. This work indicates the viability of appropriate training for increasing understanding of the implications of sample selection mechanisms.

When and how do people detect and adjust for sample bias? The present work examines how people use a small, potentially biased sample to reason about the population from which the sample was drawn. We developed a task to explore when and how people generalize from selectively sampled evidence and which cues learners use to infer the presence of selection bias and adjust their responses accordingly. Participants sampled data that followed several different distributions (e.g., bimodal) and constructed their own frequency distribution of a larger “population”. Participants “filled in” missing data when sample distribution observations were sparse or were inconsistent with priors about distribution shape. Human responses were compared with several computational model predictions. An averaging model captured response curves better than a normative Bayesian approach (for some distributions). Results suggest that people draw on both prior distributional expectations and observed sample distributions when generalizing from potentially biased samples.