The course covers theories of inference for visual analysis, aiming to teach students how to optimize visualizations for easy pattern finding, visualize distribution and uncertainty, evaluate effect size judgments, understand Bayesian cognition for belief updating, support causal inference, and facilitate model checks. The course teaches skills such as sensitivity to visual distance between means, investigating the mean difference heuristic, and using graphical inference as a Bayesian model check. The teaching method includes lectures and discussions. The intended audience for this course is individuals interested in visual analysis, data visualization, and statistical inference.
Overview
Syllabus
Introduction.
Visualizations help offload cognition to perception.
State-of-the-art: Optimize for easy pattern finding.
Approaches to visualize distribution or uncertainty.
Error bars/intervals violate expressiveness.
Probability encodings often hard to read accurately.
Frequency based uncertainty visualization.
Evaluation: Effect size judgments.
Mean difference heuristic in effect size judgments.
Sensitivity to visual distance between means.
Investigating the mean difference heuristic.
Uncertainty visualization conditions.
Bayesian cognition to understand belief updating.
How well do visualizations support causal inference?.
How does visualization support learning in analysis?.
Good visualizations facilitate model checks.
Implicit model checks give graphs their meaning.
Graphical inference as Bayesian model check.
Tableau-like system supporting model checks.
From visualization to implied model.
Open questions.
What characterizes good inference problems for vis?.
Bounding the value of a more expressive visualization.
Taught by
Stanford Online
