Statistical discrimination relies on people inferring unobservable characteristics of group members based on their beliefs about the group. Across four pre-registered experiments (N = 9,002), we show that accurate information about the composition of top performers can induce incorrect beliefs about performance differences across groups when the groups are of unequal size. Because people fail to account for base rates, they underestimate the performance of individuals from smaller groups. As a result, when participants in our experiments receive true information about the gender composition of top performers in a male-dominated candidate pool, they are less likely to hire women, even when there are no gender differences in performance (Study 1). Similarly, they are less likely to hire better-performing non-White candidates when the racial demographics of the candidate pool reflect the US population (Study 4). We show that these choices reflect an error in statistical reasoning, rather than being motivated by a desire to discriminate against any particular group (Study 2). Despite leading to less accurate beliefs, participants disproportionately seek out information about top performers when given the choice, and discrimination thus persists when information selection is endogenous (Study 3).

Lack of trust is a key barrier to collaboration in organizations and is exacerbated in contexts when employees subscribe to different ideological beliefs. Across five preregistered experiments, we find that people judge ideological opponents as more trustworthy when these opponents support their opinions with self-revealing personal narratives than when they support their opinions with either data or with stories about third parties—even when the content of the messages is carefully controlled to be consistent. Trust does not suffer when explanations grounded in personal narratives are augmented with data, suggesting that our results are not driven by quantitative aversion. Perceptions of trustworthiness are mediated by the speaker's apparent vulnerability and are greater when the self-revelation is of a more sensitive nature. Consequently, people are more willing to collaborate with ideological opponents who support their views by embedding data in a self-revealing personal narrative, rather than relying on data-only explanations. We discuss the implications of these results for future research on trust as well as for organizational practice.

Many situations involving exploration, such as businesses expanding into new products or locations, expose the explorer to the potential for subjective losses. How does the potential to experience losses during the course of a search affect individuals' appetite for exploration? In three incentivized studies, we manipulate search outcomes by presenting participants either with a gain-only environment or a gain-loss environment. The two environments offer objectively identical incentives for exploration: using a framing manipulation, we decrease gain-loss payoffs and provide participants an initial endowment to offset the difference. Participants decide how to explore a one-dimensional space, receiving payoffs based on their location each period. We predict and find that participants are motivated to avoid losses, which increases exploration when they are incurring losses, but decreases exploration when they face the potential for losses. We conclude that exploration is driven by hope of potential gains, constrained by fear of potential losses, and motivated by avoidance of experienced losses.