  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 should 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 should be
tested against outcomes of interactive analysis by people to drive theory
development.
