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Exploring the Effects of Aggregation Choices on Untrained Visualization Users' Generalizations From Data
Computer Graphics Forum ( IF 2.7 ) Pub Date : 2020-02-29 , DOI: 10.1111/cgf.13902 F. Nguyen 1 , X. Qiao 1 , J. Heer 2 , J. Hullman 1
Computer Graphics Forum ( IF 2.7 ) Pub Date : 2020-02-29 , DOI: 10.1111/cgf.13902 F. Nguyen 1 , X. Qiao 1 , J. Heer 2 , J. Hullman 1
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Visualization system designers must decide whether and how to aggregate data by default. Aggregating distributional information in a single summary mark like a mean or sum simplifies interpretation, but may lead untrained users to overlook distributional features. We ask, How are the conclusions drawn by untrained visualization users affected by aggregation strategy? We present two controlled experiments comparing generalizations of a population that untrained users made from visualizations that summarized either a 1000 record or 50 record sample with either single mean summary mark, a disaggregated view with one mark per observation or a view overlaying a mean summary mark atop a disaggregated view. While we observe no reliable effect of aggregation strategy on generalization accuracy at either sample size, users of purely disaggregated views were slightly less confident in their generalizations on average than users whose views show a single mean summary mark, and less likely to engage in dichotomous thinking about effects as either present or absent. Comparing results from 1000 record to 50 record data set, we see a considerably larger decrease in the number of generalizations produced and reported confidence in generalizations among viewers who saw disaggregated data relative to those who saw only mean summary marks.
中文翻译：
探索聚合选择对未经训练的可视化用户从数据概括的影响
可视化系统设计人员必须决定是否以及如何默认聚合数据。在单个汇总标记中聚合分布信息（如均值或总和）可以简化解释，但可能会导致未经培训的用户忽略分布特征。我们问，未经训练的可视化用户得出的结论如何受到聚合策略的影响？我们提出了两个对照实验，比较未受过训练的用户通过可视化总结了 1000 条记录或 50 条记录样本的总体概括，这些样本具有单个平均摘要标记、每个观察一个标记的分解视图或覆盖平均摘要标记的视图一个分解的观点。虽然我们没有观察到聚合策略对任一样本大小的泛化准确度的可靠影响，与视图显示单一平均摘要标记的用户相比，纯粹分类视图的用户对其概括的信心略低，并且不太可能对存在或不存在的影响进行二分法思考。将 1000 条记录与 50 条记录数据集的结果进行比较，我们看到，与只看到平均摘要标记的观看者相比，看到分解数据的观看者产生的概括数量和报告的概括信心的下降幅度要大得多。
更新日期：2020-02-29
中文翻译：
探索聚合选择对未经训练的可视化用户从数据概括的影响
可视化系统设计人员必须决定是否以及如何默认聚合数据。在单个汇总标记中聚合分布信息（如均值或总和）可以简化解释，但可能会导致未经培训的用户忽略分布特征。我们问，未经训练的可视化用户得出的结论如何受到聚合策略的影响？我们提出了两个对照实验，比较未受过训练的用户通过可视化总结了 1000 条记录或 50 条记录样本的总体概括，这些样本具有单个平均摘要标记、每个观察一个标记的分解视图或覆盖平均摘要标记的视图一个分解的观点。虽然我们没有观察到聚合策略对任一样本大小的泛化准确度的可靠影响，与视图显示单一平均摘要标记的用户相比，纯粹分类视图的用户对其概括的信心略低，并且不太可能对存在或不存在的影响进行二分法思考。将 1000 条记录与 50 条记录数据集的结果进行比较，我们看到，与只看到平均摘要标记的观看者相比，看到分解数据的观看者产生的概括数量和报告的概括信心的下降幅度要大得多。
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