Proper Bayesian reasoning is critical in a variety of domains that require practitioners to make predictionsabout the probability of events contingent upon earlier actions or events. However, much research onjudgment has shown that people who are unfamiliar with Bayes’ Theorem often reason quite poorly withconditional probabilities due to various cognitive biases. Owing to previous successes of visualizationtechniques for debiasing judges and improving judgment performance, we created an interactive computervisualization designed to aid Bayes-na?ve people in solving conditional probability problems that would notrequire a training period to use, and would be flexible enough to accommodate many problem types. Resultsare suggestive that participants using our interactive visualization were able to substantially improve theirBayesian reasoning performance above that of previous debiasing methods. This finding has significantimplications for expanding the toolbox of techniques that can be used to more accurately elicit predictionsand forecasts from judges whose expertise lies beyond the realm of statistics.
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