title: Teaching Bayesian reasoning: an evaluation of a classroom tutorial for medical students.
abstract: How likely is a diagnosis, given a particular medical test result? This probability can be determined by using Bayes's rule; however, previous research has shown that doctors often experience problems with Bayesian inferences. These findings illustrate the need to teach statistical reasoning in medical education. A new method of teaching Bayesian reasoning is representation learning: the key idea is to instruct medical students how to translate probability information into a representation that is easier to process, namely natural frequencies. This approach was implemented in a one-hour classroom tutorial to evaluate its effectiveness in this setting and compared with a traditional rule-learning approach. Evaluation took place two months after training by testing students' ability to correctly solve a Bayesian inference task with information represented as probabilities. While both approaches improved performance, almost three times as many students were able to profit from representation training as opposed to rule training.
authors.affiliation: Max Planck Institute for Human Development, Center for Adaptive Behavior and Cognition, Berlin, Germany. kurzenh@mpib-berlin.mpg.de
authors.forename: Stephanie
authors.initials: S
authors.lastname: Kurzenhäuser
authors.forename: Ulrich
authors.initials: U
authors.lastname: Hoffrage
mesh_terms: D000328:Adult; D001499:Bayes Theorem; D002983:Clinical Competence; D003479:Curriculum; D003657:Decision Making; D004504:Education, Medical, Undergraduate / Q000379:methods*; D005858:Germany; D006801:Humans; D008128:Logic; D013337:Students, Medical; D013663:Teaching / Q000379:methods*