Is A 70% Forecast more Accurate than a 30% Forecast? How Level of a Forecast Affects Inferences about Forecasts and Forecasters

Abstract

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.