Should We or Should We Not Include Confidence Intervals in COVID-19 Death Forecasting? Evidence from a Survey Experiment

Forecasting during the COVID-19 pandemic entails a great deal of uncertainty. The same way that we would like electoral forecasters to systematically include their confidence intervals to account for such uncertainty, we assume that COVID-19-related forecasts should follow that norm. Based on literature on negative bias, we may expect the presence of uncertainty to affect citizens’ attitudes and behaviours, which would in turn have major implications on how we should present these sensitive forecasts. In this research we present the main findings of a survey experiment where citizens were exposed to a projection of the total number of deaths. We manipulated the exclusion (and inclusion) of graphically depicted confidence intervals in order to isolate the average causal effect of uncertainty. Our results show that accounting for uncertainty does not change (1) citizens’ perceptions of projections’ reliability, nor does it affect (2) their support for preventive public health measures. We conclude by discussing the implications of our findings.