Loss aversion is one of the most prominent concepts in the study of risky decision making. Traditionally, research on loss aversion has utilized paradigms in which participants choose between risky options whose payoff distributions are given as summary descriptions (decisions from description). But to what extent does loss aversion also emerge in situations in which the payoff distributions are learned from experiential sampling—that is, in decisions from experience? To address this question, we conducted a meta-analysis, re-analyzing and synthesizing all existing datasets (total n > 430 per condition) that allow for a direct comparison of decisions from description and decisions from experience based on mixed gambles (i.e., where the options can lead to either a gain or a loss). Analyzing the choices with cumulative prospect theory in a Bayesian framework suggests a higher level of loss aversion in decisions from experience than in decisions from description. Possible mechanisms leading to this novel description-experience gap, such as asymmetric reinforcement learning during the sampling phase, are discussed.