How to Train Novices in Bayesian Reasoning

Bayesian Reasoning is both a fundamental idea of probability and a key model in applied sciences for evaluating situations of uncertainty. Bayesian Reasoning may be defined as the dealing with, and understanding of, Bayesian situations. This includes various aspects such as calculating a conditional probability (performance), assessing the effects of changes to the parameters of a formula on the result (covariation) and adequately interpreting and explaining the results of a formula (communication). Bayesian Reasoning is crucial in several non-mathematical disciplines such as medicine and law. However, even experts from these domains struggle to reason in a Bayesian manner. Therefore, it is desirable to develop a training course for this specific audience regarding the different aspects of Bayesian Reasoning. In this paper, we present an evidence-based development of such training courses by considering relevant prior research on successful strategies for Bayesian Reasoning (e.g., natural frequencies and adequate visualizations) and on the 4C/ID model as a promising instructional approach. The results of a formative evaluation are described, which show that students from the target audience (i.e., medicine or law) increased their Bayesian Reasoning skills and found taking part in the training courses to be relevant and fruitful for their professional expertise.

Automated summary

Bayesian Reasoning, a fundamental idea in probability, is crucial for evaluating uncertain situations. It involves calculating conditional probabilities, assessing parameter changes on results, and explaining formula outcomes, and is particularly important in non-mathematical fields like medicine and law. Developing training courses on Bayesian Reasoning specific to these professions is necessary. Evidence-based research shows that students from medicine and law backgrounds can improve their Bayesian Reasoning skills through such courses, enhancing their professional expertise.