Abstract

People made forecasts from graphically presented time series. Series were sinusoids overlaid by a zero or positive linear trend and a zero, low, moderate, or high level of noise. Forecasting performance was affected by both these variables. However, it did not correlate with ability to identify the trend and correlated significantly with ability to detect the sinusoidal pattern only when series were noise-free. A second experiment showed that the effect of data noise was not influenced by the number of forecasts that people made from a series. These findings are consistent with the view that data noise does not affect the way that forecasts are made but that it impairs them for two reasons. First, it renders the anchor-and-adjust heuristics underlying them less effective. Second, it causes people to add more noise to their judgements in their attempts to make them representative of the data.