How Do Predictions Affect Accuracy Perceptions? The Role of Depth of Information Analyses

Information is key to decision-making. However, seldom do consumers have the time or the ability to interpret information. Therefore, they often rely on forecasts that analysts (e.g., market researchers) make using this information. But how do consumers evaluate the accuracy of these predictions? We propose a new cue: the forecast’s probability. We posit that when the forecaster makes a prediction (e.g. a 70% or 30% chance that a stock will increase in value), people will use the estimate (70% or 30%) to assess accuracy. This accuracy attribution occurs because people infer from a higher prediction that the forecaster has conducted more in depth analyses of the available information when calculating the prediction. This arises from a naïve belief that a more in depth analysis can increase prediction accuracy. Because people infer that a higher (e.g. 70%) prediction reflects greater predictability relative to a lower prediction (e.g. 30%), individuals will believe that the forecaster has conducted a more thorough analysis when making a 70% prediction relative to a 30% prediction (H1). If higher (vs. lower) predictions signal that a more in-depth analysis has been conducted, then the resulting estimate should also be more accurate (H2). Furthermore, the relationship between prediction estimates and accuracy will be mediated by perceptions of depth of information analyses (H3).