Abstract
Proper Bayesian reasoning is critical in a variety of domains that require practitioners to make predictions about the probability of events contingent upon earlier actions or events. However, much research on judgment has shown that people who are unfamiliar with Bayes’ Theorem often reason quite poorly with conditional probabilities due to various cognitive biases. Owing to previous successes of visualization techniques for debiasing judges and improving judgment performance, we created an interactive computer visualization designed to aid Bayes-naïve people in solving conditional probability problems that would not require a training period to use, and would be flexible enough to accommodate many problem types. Results are suggestive that participants using our interactive visualization were able to substantially improve their Bayesian reasoning performance above that of previous debiasing methods. This finding has significant implications for expanding the toolbox of techniques that can be used to more accurately elicit predictions and forecasts from judges whose expertise lies beyond the realm of statistics.
Get full access to this article
View all access options for this article.