The influence of graphical format on judgmental forecasting accuracy: Lines versus points

Abstract

People made forecasts from real data series. The points in the series were un-trended and independent. Hence, forecasts should have been on the mean value. However, consistent with previous research on forecasting biases, forecasts were too close to the last data point. It appears that forecasters see positive sequential dependence where none exists. In three experiments, we examined this bias in different types of forecasting task: point forecasting, probability density forecasting, and interval forecasting. In all cases, we found that it was greater when the data series were displayed using continuous line graphs than when it was displayed using discrete point graphs. Consistent with arguments made by Zacks and Tversky (Memory and Cognition, 27:1073, 1999), we suggest that people are more likely to group data together and to see patterns in them when those data are presented in a continuous than in a discrete format. These findings have implications for forecasting practice.