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An Eye-Tracking Study of Statistical Reasoning With Tree Diagrams and 2 × 2 Tables

机译:用树图和2×2表进行统计推理的眼动研究

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摘要

Changing the information format from probabilities into frequencies as well as employing appropriate visualizations such as tree diagrams or 2 × 2 tables are important tools that can facilitate people’s statistical reasoning. Previous studies have shown that despite their widespread use in statistical textbooks, both of those visualization types are only of restricted help when they are provided with probabilities, but that they can foster insight when presented with frequencies instead. In the present study, we attempt to replicate this effect and also examine, by the method of eye tracking, why probabilistic 2 × 2 tables and tree diagrams do not facilitate reasoning with regard to Bayesian inferences (i.e., determining what errors occur and whether they can be explained by scan paths), and why the same visualizations are of great help to an individual when they are combined with frequencies. All ten inferences of N = 24 participants were based solely on tree diagrams or 2 × 2 tables that presented either the famous “mammography context” or an “economics context” (without additional textual wording). We first asked participants for marginal, conjoint, and (non-inverted) conditional probabilities (or frequencies), followed by related Bayesian tasks. While solution rates were higher for natural frequency questions as compared to probability versions, eye-tracking analyses indeed yielded noticeable differences regarding eye movements between correct and incorrect solutions. For instance, heat maps (aggregated scan paths) of distinct results differed remarkably, thereby making correct and faulty strategies visible in the line of theoretical classifications. Moreover, the inherent structure of 2 × 2 tables seems to help participants avoid certain Bayesian mistakes (e.g., “Fisherian” error) while tree diagrams seem to help steer them away from others (e.g., “joint occurrence”). We will discuss resulting educational consequences at the end of the paper.
机译:将信息格式从概率更改为频率,并采用适当的可视化形式(例如树形图或2×2表格),是可以帮助人们进行统计推理的重要工具。先前的研究表明,尽管在可视化教科书中广泛使用了这两种可视化类型,但只有在提供概率时,这两种可视化类型的帮助才有限,但是当出现频率时,它们可以促进洞察。在本研究中,我们尝试复制这种效果,并通过眼动追踪的方法检查为什么概率2×2表格和树形图不便于就贝叶斯推断进行推理(即确定发生什么错误以及是否发生错误) (可以通过扫描路径来解释),以及为什么将相同的可视化与频率结合起来对个人有很大帮助? N = 24位参与者的所有十个推论都完全基于树状图或2×2表格,这些表格呈现了著名的“乳腺X线摄影背景”或“经济学背景”(没有其他文字说明)。我们首先要求参与者提供边际,联合和(不可逆)条件概率(或频率),然后是相关的贝叶斯任务。尽管与概率版本相比,自然频率问题的解决率更高,但眼动分析确实在正确和错误解决方案之间的眼动方面产生了明显的差异。例如,不同结果的热图(汇总的扫描路径)显着不同,从而使正确和错误的策略在理论分类中可见。此外,2×2表格的固有结构似乎可以帮助参与者避免某些贝叶斯错误(例如“ Fisherian”错误),而树形图似乎可以帮助他们远离其他错误(例如“联合出现”)。我们将在本文结尾处讨论由此产生的教育后果。

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