Section of Hospital Medicine, Division of General Internal Medicine, Weill Cornell Medical College, 525 East 68th Street, Box 331, New York, NY 10065, USA; Division of Hospital Medicine, University of Colorado School of Medicine, Aurora, CO, USA.
Section of Hospital Medicine, Division of General Internal Medicine, Weill Cornell Medical College, 525 East 68th Street, Box 331, New York, NY 10065, USA.
J Clin Epidemiol. 2020 Jun;122:49-55. doi: 10.1016/j.jclinepi.2020.03.003. Epub 2020 Mar 10.
The aim of the tutorial is to help educators address misconceptions about P values and provide a tool that can be used to teach a more contemporary interpretation.
A scripted tutorial using problem-based learning and a diagnostic test analogy to deconstruct the misunderstandings about P values and develop a more Bayesian approach to study interpretation.
A diagnostic test analogy is an effective teaching tool. Learners' understanding of Bayes' theorem in diagnostic testing can be used as a bridge to the realization that the prestudy probability of a true difference is crucial for study interpretation. The analogy has several caveats and shortcomings. The limitations of this analogy and the conceptual difficulties with the Bayesian study analyses are addressed.
P values do not provide the information many assume they do-they are not equivalent to a probability of a chance finding. This tutorial helps move learners from these incorrect notions to new insights.
本教程旨在帮助教育者纠正对 P 值的误解，并提供一种工具，以教授更具现代性的解释。
使用基于问题的学习和诊断测试类比的脚本教程，解构对 P 值的误解，并采用更贝叶斯的方法来解释研究。
诊断测试类比是一种有效的教学工具。学习者对诊断测试中贝叶斯定理的理解可以作为桥梁，使他们认识到研究解释中真实差异的先验概率至关重要。该类比存在一些注意事项和缺点。本文讨论了这种类比的局限性以及贝叶斯研究分析的概念性困难。
P 值并不提供许多人认为的信息——它们不等同于偶然发现的概率。本教程帮助学习者从这些错误的观念转变为新的见解。
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