How to improve people's interpretation of probabilities of precipitation

Most research into uncertainty focuses on how people estimate probability magnitude. By contrast, this paper focuses on how people interpret the concept of probability and why they often misinterpret it. In a weather forecast context, we hypothesised that the absence of an explicit reference class and the polysemy of the percentage format are causing incorrect probability interpretations, and test two interventions to help people make better probability interpretation. In two studies (N = 1337), we demonstrate that most people from the UK and the US do not interpret probabilities of precipitation correctly. The explicit mention of the reference class helped people to interpret probabilities of precipitation better when the target area was explicit; but this was not the case when it was not specified. Furthermore, the polysemy of the percentage format is not likely to cause these misinterpretations, since a non-polysemous format (e.g. verbal probability) did not facilitate a correct probability interpretation in our studies. A Bayes factor analysis supported both of these conclusions. We discuss theoretical and applied implications of our findings.