How to Train Novices in Bayesian Reasoning

Theresa Büchter, Andreas Eichler, Nicole Steib, Karin Binder, Katharina Böcherer-Linder, Stefan Krauss and Markus Vogel
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Theresa Büchter: Institute of Mathematics, University of Kassel, 34132 Kassel, Germany
Andreas Eichler: Institute of Mathematics, University of Kassel, 34132 Kassel, Germany
Nicole Steib: Faculty of Mathematics, University of Regensburg, 93053 Regensburg, Germany
Karin Binder: Institute of Mathematics, Ludwig-Maximilians-University Munich, 80333 München, Germany
Katharina Böcherer-Linder: Department of Mathematics Education, University of Freiburg, 79104 Freiburg, Germany
Stefan Krauss: Faculty of Mathematics, University of Regensburg, 93053 Regensburg, Germany
Markus Vogel: Institute of Mathematics, University of Education Heidelberg, 69120 Heidelberg, Germany

Mathematics, 2022, vol. 10, issue 9, 1-31

Abstract: 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.

Keywords: Bayesian Reasoning; Bayes’ rule; visualization; unit square; double tree; natural frequencies; 4C/ID model (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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