Proceedings of the Annual Meeting of the Cognitive Science Society

usually collect information to serve specific goals andoften end up with samples that are unrepresentative of the un-derlying population. This can introduce biases on later judg-ments that generalize from these samples. Here we show thatgoals influence not only what information we collect, but alsowhen we decide to terminate search. Using an optimal stop-ping analysis, we demonstrate that even when learners have nocontrol over the content of a sample (i.e., natural sampling),the simple decision of when to stop sampling can yield sampledistributions that are non-representative and could potentiallybias future decision making. We test the prediction of thesetheoretical analyses with two behavioral experiments