A consumer’s decision to engage in search depends on the beliefs the consumer has about an unknown product characteristic such as price. Given beliefs are rarely observed, researchers typically assume that consumers have rational expectations or update beliefs consistent with Bayesian updating. These assumptions are not only restrictive, but additionally, do not afford the researcher, or the retailer, an opportunity to price discriminate among consumers based on heterogeneity in beliefs. We first show, through Monte Carlo experiments, how these assumptions impact estimates of search cost. Next, we design an incentive-aligned online study where subjects search over the price of a homogeneous good, and we elicit distributions of price beliefs before and after each search. Based on data collected from a nationally representative panel, we find substantial heterogeneity in prior price beliefs. We find that subjects update their beliefs in response to search outcomes, but they deviate from Bayesian updating in that they under-react to new information. Importantly, we show that (i) assuming Bayesian updating does not significantly bias search cost estimates at the aggregate level provided the researcher accounts for heterogeneous prior beliefs, (ii) eliciting heterogeneity in prior expected prices is much more important than eliciting heterogeneity in prior price uncertainty, and (iii) a retailer can increase profits through third-degree price discrimination by recognizing the heterogeneity in prior beliefs.