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Effects of visualizing statistical information a?? an empirical study on tree diagrams and 2 ?? 2 tables

机译:可视化统计信息的作用关于树形图和2的实证研究? 2桌

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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.
机译:在他们的研究文章中,学者经常使用2×2的表格或包含自然频率的树图来向同伴说明贝叶斯推理情况。有趣的是,到目前为止,这些可视化对参与者绩效的影响还没有经过经验检验(除了明确的培训研究之外)。在本文中,我们报告了一项实证研究(3×2×2设计),其中我们系统地改变了可视化(无可视化与2×2表格与树形图)和信息格式(概率与自然频率)两种环境(医学与经济环境;不是感兴趣的因素)。 N = 259名参与者(16至18岁的学生)中的每名参与者都必须解决两个典型的贝叶斯推理任务(“乳腺摄影问题”和“经济学问题”)。假设是2×2的表格和树形图-尤其是当包含自然频率时-可以促进对贝叶斯推理情形众所周知的困难结构的洞察力。与许多其他可视化效果(例如图标阵列,欧拉图)相比,2×2表格和树图的优点是可以轻松构建它们。将讨论我们的发现对教授贝叶斯推理的意义。

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