Visual analytics (VA) tools support data exploration by helping analysts quickly and iteratively generate views of data which reveal interesting patterns. However, these tools seldom enable explicit checks of the resulting interpretations of data -- e.g., whether patterns can be accounted for by a model that implies a particular structure in the relationships between variables. We present EVM, a data exploration tool that enables users to express and check provisional interpretations of data in the form of statistical models. EVM integrates support for visualization-based model checks by rendering distributions of model predictions alongside user-generated views of data. In a user study with data scientists practicing in the private and public sector, we evaluate how model checks influence analysts' thinking during data exploration. Our analysis characterizes how participants use model checks to scrutinize expectations about data generating process and surfaces further opportunities to scaffold model exploration in VA tools.

翻译：可视化分析（VA）工具通过帮助分析人员快速、迭代地生成数据视图以揭示有意义的模式，从而支持数据探索。然而，这类工具很少能够对数据解释结果进行显式检验——例如，某个模式是否能被一个暗示变量间具有特定结构关系的模型所解释。我们提出EVM，一种数据探索工具，使用户能够以统计模型的形式表达并检验对数据的临时性解释。EVM通过将模型预测的分布呈现于用户生成的数据视图旁，实现了对基于可视化的模型检验的支持。在一项针对私营与公共部门数据科学家的用户研究中，我们评估了模型检验如何影响分析人员的数据探索思维过程。我们的分析揭示了参与者如何利用模型检验审视其关于数据生成过程的假设，并指出了在VA工具中进一步支持模型探索的潜在方向。