Kunzelmann Alexandra K, Binder Karin, Fischer Martin R, Reincke Martin, Braun Leah T, Schmidmaier Ralf
Department of Internal Medicine IV, University Hospital, LMU Munich, Germany.
Mathematics Education, LMU Munich, Munchen, Bayern, Germany.
MDM Policy Pract. 2022 Mar 16;7(1):23814683221086623. doi: 10.1177/23814683221086623. eCollection 2022 Jan-Jun.
Medical students often have problems with Bayesian reasoning situations. Representing statistical information as natural frequencies (instead of probabilities) and visualizing them (e.g., with double-trees or net diagrams) leads to higher accuracy in solving these tasks. However, double-trees and net diagrams (which already contain the correct solution of the task, so that the solution could be read of the diagrams) have not yet been studied in medical education. This study examined the influence of information format (probabilities v. frequencies) and visualization (double-tree v. net diagram) on the accuracy and speed of Bayesian judgments. A total of 142 medical students at different university medical schools (Munich, Kiel, Goettingen, Erlangen, Nuremberg, Berlin, Regensburg) in Germany predicted posterior probabilities in 4 different medical Bayesian reasoning tasks, resulting in a 3-factorial 2 × 2 × 4 design. The diagnostic efficiency for the different versions was represented as the median time divided by the percentage of correct inferences. Frequency visualizations led to a significantly higher accuracy and faster judgments than did probability visualizations. Participants solved 80% of the tasks correctly in the frequency double-tree and the frequency net diagram. Visualizations with probabilities also led to relatively high performance rates: 73% in the probability double-tree and 70% in the probability net diagram. The median time for a correct inference was fastest with the frequency double tree (2:08 min) followed by the frequency net diagram and the probability double-tree (both 2:26 min) and probability net diagram (2:33 min). The type of visualization did not result in a significant difference. Frequency double-trees and frequency net diagrams help answer Bayesian tasks more accurately and also more quickly than the respective probability visualizations. Surprisingly, the effect of information format (probabilities v. frequencies) on performance was higher in previous studies: medical students seem also quite capable of identifying the correct solution to the Bayesian task, among other probabilities in the probability visualizations.
Frequency double-trees and frequency nets help answer Bayesian tasks not only more accurately but also more quickly than the respective probability visualizations.In double-trees and net diagrams, the effect of the information format (probabilities v. natural frequencies) on performance is remarkably lower in this high-performing sample than that shown in previous studies.
医学生在贝叶斯推理情境中常常遇到问题。将统计信息表示为自然频率(而非概率)并进行可视化处理(例如,使用双树图或网状图),在解决这些任务时能提高准确性。然而,双树图和网状图(它们已经包含了任务的正确解决方案,因此可以从图中读取答案)在医学教育中尚未得到研究。本研究考察了信息格式(概率与频率)和可视化方式(双树图与网状图)对贝叶斯判断的准确性和速度的影响。德国不同大学医学院(慕尼黑、基尔、哥廷根、埃尔朗根、纽伦堡、柏林、雷根斯堡)的142名医学生在4个不同的医学贝叶斯推理任务中预测后验概率,形成了一个三因素2×2×4设计。不同版本的诊断效率用中位数时间除以正确推理的百分比来表示。频率可视化比概率可视化能显著提高准确性并加快判断速度。参与者在频率双树图和频率网状图中正确解决了80%的任务。概率可视化也有相对较高的正确率:概率双树图为73%,概率网状图为70%。正确推理的中位数时间以频率双树图最快(2分08秒),其次是频率网状图和概率双树图(均为2分26秒)以及概率网状图(2分33秒)。可视化方式类型没有导致显著差异。频率双树图和频率网状图比各自的概率可视化更能准确且快速地回答贝叶斯任务。令人惊讶的是,在之前的研究中信息格式(概率与频率)对表现的影响更大:医学生似乎也相当有能力在概率可视化中的其他概率中识别出贝叶斯任务的正确解决方案。
频率双树图和频率网状图比各自的概率可视化不仅能更准确地回答贝叶斯任务,而且速度更快。在双树图和网状图中,对于这个高绩效样本,信息格式(概率与自然频率)对表现的影响明显低于之前研究中所显示的。