LAUSR

Bar graphs can improve risk communication in medicine and health. Unfortunately, recent research has revealed that bar graphs are associated with a robust bias that can lead to systematic judgement and decision-making errors. When people view bar graphs representing means, they tend to believe that data points located within bars are more likely to be part of the underlying distributions than equidistant points outside bars. In three experiments, we investigated potential consequences, key cognitive mechanisms, and generalisability of the within-the-bar bias in the medical domain. We also investigated the effectiveness of different interventions to reduce the effect of this bias and protect people from errors. Results revealed that the within-the-bar bias systematically affected participants’ judgements and decisions concerning treatments for controlling blood glucose, as well as their interpretations of ecological graphs designed to guide health policy decisions. Interestingly, individuals with higher graph literacy showed the largest biases. However, the use of dot plots to replace bars improved the accuracy of interpretations. Perceptual mechanisms underlying the within-the-bar bias and prescriptive implications for graph design are discussed.