Theories of Inference for Data Interactions
27th May 2021, 4pm CET
Online Talk: https://bbb.lri.fr/b/pet-4wc-dt3
Research and development in computer science and statistics have produced increasingly sophisticated software interfaces for interactive and exploratory analysis, optimized for easy pattern finding and data exposure. But design philosophies that emphasize exploration over other phases of analysis risk confusing a need for flexibility with a conclusion that exploratory visual analysis is inherently “model free” and cannot be formalized. We describe how without a grounding in theories of human statistical inference, research in exploratory visual analysis can lead to contradictory interface objectives and representations of uncertainty that can discourage users from drawing valid inferences. We discuss how the concept of a model check in a Bayesian statistical framework unites exploratory and confirmatory analysis, and how this understanding relates to other proposed theories of graphical inference. Viewing interactive analysis as driven by model checks suggests new directions for software and empirical research around exploratory and visual analysis. For example, systems might enable specifying and explicitly comparing data to null and other reference distributions and better representations of uncertainty. Implications of Bayesian and other theories of graphical inference can be tested against outcomes of interactive analysis by people to drive theory development.
Jessica Hullman is an Associate Professor of Computer Science at Northwestern University. Her research looks at how to design, evaluate, coordinate, and think about representations of data for amplifying cognition and decision making. She co-directs the Midwest Uncertainty Collective, a lab devoted to better representations, evaluations, and theory around data interfaces in data, with Matt Kay. Jessica is the recipient of a Microsoft Faculty Fellowship, NSF CAREER Award, and multiple best papers at top visualization and human-computer interaction conferences.