Kurzenhäuser Stephanie, Hoffrage Ulrich
Max Planck Institute for Human Development, Center for Adaptive Behavior and Cognition, Berlin, Germany.
Med Teach. 2002 Sep;24(5):516-21. doi: 10.1080/0142159021000012540.
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.
给定特定的医学检测结果，做出诊断的可能性有多大？这个概率可以通过贝叶斯法则来确定；然而，先前的研究表明，医生在贝叶斯推理方面常常遇到问题。这些发现表明在医学教育中教授统计推理的必要性。一种教授贝叶斯推理的新方法是表征学习：关键思想是指导医学生如何将概率信息转化为更易于处理的表征，即自然频率。这种方法在一小时的课堂教程中实施，以评估其在这种情况下的有效性，并与传统的规则学习方法进行比较。在训练两个月后进行评估，通过测试学生用概率形式表示的信息正确解决贝叶斯推理任务的能力。虽然两种方法都提高了表现，但与规则训练相比，几乎有三倍之多的学生能够从表征训练中受益。
