Countless policies are crafted with the intention of punishing all who do wrong or rewarding only those who do right. However, this requires accommodating certain mistakes: some who do not deserve to be punished might be, and some who deserve to be rewarded might not be. Six preregistered experiments (N = 3,484 U.S. adults) reveal that people are more willing to accept this trade-off in principle, before errors occur, than in practice, after errors occur. The result is an asymmetry such that for punishments, people believe it is more important to prevent false negatives (e.g., criminals escaping justice) than to fix them, and more important to fix false positives (e.g., wrongful convictions) than to prevent them. For rewards, people believe it is more important to prevent false positives (e.g., welfare fraud) than to fix them, and more important to fix false negatives (e.g., improperly denied benefits) than to prevent them.

People expressing uncertain values frequently offer point predictions or intervals of varying widths (e.g., “I’d say your investment will earn $6,000” versus “I’d say your investment will earn $5,000–$7,000”). Interestingly, prior research has drawn conflicting conclusions concerning the credibility of more versus less precise predictions. We resolve this inconsistency by identifying a key moderator: the perceived nature of uncertainty. Specifically, we find that more precise predictions are more credible under epistemic (“knowable”) uncertainty because they convey experts’ degree of confidence. Under aleatory (“random”) uncertainty, however, less precise predictions are more credible — if they are within reasonable bounds — because they convey experts’ assessments of outcome variability. These results inform literatures on both the communication of uncertainty and the interpretation of confidence intervals.

Insurance is one of the oldest and most ubiquitous financial products, and numerous industries rely on healthy insurance markets to function. Actuarially, insurance premiums largely depend on the likelihood of making claims (“risk levels”) and the size of potential claims (“coverage needs”). Consequently, when risk levels and coverage needs increase — e.g., when the frequency and severity of natural disasters increase due to climate change — so too should premiums. Yet consumers typically believe it is unfair for insurers to increase premiums, triggering a host of market distortions (and even market failure). How, then, do consumers believe insurance should be priced? Across 10 preregistered experiments (N = 6,379), we show that consumers view it as less fair for insurers to increase premiums when risk levels increase than when coverage needs increase, holding expected value constant. Additionally, we explore the implications of this distinction for purchase intentions, the acceptability of price discrimination, and support for insurance mandates. Finally, we propose a novel mechanism to explain these beliefs: risk levels may feel less personally controllable than coverage needs. As such, our work not only sheds new light on the psychology of insurance but also offers new insights into the antecedents of price fairness.