Binder Karin, Krauss Stefan, Bruckmaier Georg
Mathematics Education, Faculty of Mathematics, University of Regensburg Regensburg, Germany.
Front Psychol. 2015 Aug 26;6:1186. doi: 10.3389/fpsyg.2015.01186. eCollection 2015.
In their research articles, scholars often use 2 × 2 tables or tree diagrams including natural frequencies in order to illustrate Bayesian reasoning situations to their peers. Interestingly, the effect of these visualizations on participants' performance has not been tested empirically so far (apart from explicit training studies). In the present article, we report on an empirical study (3 × 2 × 2 design) in which we systematically vary visualization (no visualization vs. 2 × 2 table vs. tree diagram) and information format (probabilities vs. natural frequencies) for two contexts (medical vs. economical context; not a factor of interest). Each of N = 259 participants (students of age 16-18) had to solve two typical Bayesian reasoning tasks ("mammography problem" and "economics problem"). The hypothesis is that 2 × 2 tables and tree diagrams - especially when natural frequencies are included - can foster insight into the notoriously difficult structure of Bayesian reasoning situations. In contrast to many other visualizations (e.g., icon arrays, Euler diagrams), 2 × 2 tables and tree diagrams have the advantage that they can be constructed easily. The implications of our findings for teaching Bayesian reasoning will be discussed.
在他们的研究文章中,学者们经常使用2×2表格或包含自然频率的树形图,以便向同行阐述贝叶斯推理情境。有趣的是,到目前为止,这些可视化工具对参与者表现的影响尚未得到实证检验(除了明确的训练研究)。在本文中,我们报告了一项实证研究(3×2×2设计),其中我们针对两种情境(医学情境与经济情境;非感兴趣因素)系统地改变了可视化方式(无可视化、2×2表格、树形图)和信息格式(概率与自然频率)。N = 259名参与者(年龄在16 - 18岁的学生)每人都必须解决两个典型的贝叶斯推理任务(“乳房X光检查问题”和“经济问题”)。假设是2×2表格和树形图——尤其是当包含自然频率时——能够促进对贝叶斯推理情境中 notoriously difficult(极其困难)的结构的理解。与许多其他可视化工具(例如图标阵列、欧拉图)不同,2×2表格和树形图具有易于构建的优势。我们将讨论研究结果对贝叶斯推理教学的启示。