and the JMR review team for their helpful comments and suggestions. Rik Pieters served as associate editor for this article.

Consumers routinely rely on forecasters to make predictions about uncertain events (e.g., sporting contests, stock fluctuations). The authors demonstrate that when forecasts are higher versus lower (e.g., a 70% vs. 30% chance of team A winning a game), consumers infer that the forecaster is more confident in his or her prediction, has conducted more in-depth analyses, and is more trustworthy. Consumers also judge the prediction as more accurate. This occurs because people tend to evaluate forecasts on the basis of how well they predict a target event occurring (e.g., team A winning). Higher forecasts indicate greater likelihood of the target event occurring and signal a confident analyst, while lower forecasts indicate lower likelihood and lower confidence in the target event occurring. Yet because with lower forecasts, consumers still focus on the target event (rather than its complement), lower confidence in the target event occurring is erroneously interpreted as the forecaster being less confident in his or her overall prediction (instead of more confident in the complementary event occurring, i.e., team A losing). The authors identify boundary conditions, generalize to other prediction formats, and demonstrate consequences of their findings.

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When presenting their predictions, predictors may also provide varying levels of information regarding how they arrived at their predictions. However, it is unclear what role these explanations play in the resulting evaluations of the predictors. In 3 experiments, the authors demonstrate that when a predictor provides a brief explanation, individuals evaluate the predictor less positively than when a predictor simply provides no explanation or provides a detailed explanation for their prediction. This happens because a brief explanation lacks details, from which individuals infer the predictor did not do an in-depth analysis, and judge the predictor accordingly. Without an explanation (with detailed explanation), individuals assume (infer) predictors arrive at their predictions via sufficient in-depth analysis. The authors conclude with a discussion of implications for theory and predictors as well as future directions for research.

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