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.

Abstract 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.

Understanding and accounting for uncertainty is critical to effectively reasoning about visualized data. However, evaluating the impact of an uncertainty visualization is complex due to the difficulties that people have interpreting uncertainty and the challenge of defining correct behavior with uncertainty information. Currently, evaluators of uncertainty visualization must rely on general purpose visualization evaluation frameworks which can be ill-equipped to provide guidance with the unique difficulties of assessing judgments under uncertainty. To help evaluators navigate these complexities, we present a taxonomy for characterizing decisions made in designing an evaluation of an uncertainty visualization. Our taxonomy differentiates six levels of decisions that comprise an uncertainty visualization evaluation: the behavioral targets of the study, expected effects from an uncertainty visualization, evaluation goals, measures, elicitation techniques, and analysis approaches. Applying our taxonomy to 86 user studies of uncertainty visualizations, we find that existing evaluation practice, particularly in visualization research, focuses on Performance and Satisfaction-based measures that assume more predictable and statistically-driven judgment behavior than is suggested by research on human judgment and decision making. We reflect on common themes in evaluation practice concerning the interpretation and semantics of uncertainty, the use of confidence reporting, and a bias toward evaluating performance as accuracy rather than decision quality. We conclude with a concrete set of recommendations for evaluators designed to reduce the mismatch between the conceptualization of uncertainty in visualization versus other fields.