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Research article summary (published 30 Aug 2002):

Teaching Bayesian reasoning: an evaluation of a classroom tutorial for medical students.

Full 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.

 

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Author information

Author/s: Kurzenhäuser, Stephanie (S); Hoffrage, Ulrich (U);

Affiliation: Max Planck Institute for Human Development, Center for Adaptive Behavior and Cognition, Berlin, Germany. kurzenh(-atsign-)mpib-berlin.mpg.de

Journal and publication information

Publication Type: Evaluation Studies; Journal Article; Research Support, Non-U.S. Gov't

Journal: Medical teacher (Med Teach), published in England. (Language: eng)

Reference: 2002-Sep; vol 24 (issue 5) : pp 516-21

Dates: Created 2002/11/26; Completed 2003/03/06; Revised 2006/11/15;

PMID: 12450472, status: MEDLINE (last retrieval date: 12/26/2008)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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