Social Work Bldg room 903, at 4pm on Mon 18 Sep 2023:

Evaluating Visualizations for Inference and Decision-Making

Research and development in computer science and statistics have produced increasingly sophisticated software interfaces for interactive visual data analysis. Data visualizations have also become ubiquitous for communication in the news and scientific publishing. Despite these successes, our understanding of how to design effective visualizations for data-driven decision-making remains limited. Design philosophies that emphasize data exploration and hypothesis generation can encourage pattern-finding at the expense of quantifying uncertainty. Designing visualizations to maximize perceptual accuracy and self-reported satisfaction can lead people to adopt visualizations that promote overconfident interpretations. I will motivate a few alternative objectives for measuring the effectiveness of visualization, and show how a rational agent framework based in statistical decision theory can help us understand the value of a visualization in the abstract and in light of empirical study results.

This is a super-important topic, also interesting because in many cases people think evaluation is a big deal without thinking too hard about what are the goals of the graph. It’s hard to design a good evaluation without having some goals in mind. For example, see this discussion from a few years ago of a study that was described as finding that “chartjunk is more useful than plain graphs” and this paper with Antony Unwin on different goals of infoviz and statistical and graphics.

Another thing I like about the above abstract from Jessica is how she’s talking about two different goals of statistical graphics: (1) clarifying a point that you want to convey, and (2) providing opportunity for discovery. Both are important!