When performing data analysis, people often confront data sets containing missing values. We conducted an empirical study to understand the effects of visualizing those missing values on participants' decision-making processes while performing a visual data exploration task. More specifically, our study participants purchased a hypothetical portfolio of stocks based on a dataset where some stocks had missing values for attributes such as PE ratio, beta, and EPS. The experiment used scatterplots to communicate the stock data. For one group of participants, stocks with missing values simply were not shown, while the second group saw such stocks depicted with estimated values as points with error bars. We measured participants' cognitive load involved in decision-making with data with missing values. Our results indicate that their decision-making workflow was different across two conditions.

翻译：进行数据分析时,人们常常面对包含缺失值的数据集。我们进行了一项实证研究,以了解这些缺失值在进行视觉数据探索时对参与者决策过程的可视化影响。更具体地说,我们的研究参与者根据数据集购买了一个假设的库存组合,其中某些库存在PE比率、贝塔和EPS等属性方面缺少值。实验用散射点来传送库存数据。对于一组参与者来说,缺少值的库存只是没有显示,而第二组则看到这些库存以估计值描述为误差点。我们用缺少值的数据衡量参与者在决策中的认知负荷。我们的结果显示,他们的决策工作流程在两种条件下是不同的。