In this new episode, we talk about the interplay between statistics and data visualization. We do that with Andrew Gelman, Professor of Statistics and Political Science at Columbia University, and Jessica Hullman, Professor of Computer Science at Northwestern University. Andrew started the popular blog “Statistical Modeling, Causal Inference, and Social Science,” which has an active community of readers and has been around for many years. Jessica started contributing lately with many exciting posts, several of which have to do with data visualization. In the episode, we touch upon many topics, including the story behind the blog, the role of surprises, anomalies, and storytelling in science, the Anscombe’s quartet, and exploratory data analysis.
- Jessica Hullman: http://users.eecs.northwestern.edu/~jhullman/
- Andrew Gelman: http://www.stat.columbia.edu/~gelman/
- Blog: Statistical Modeling, Causal Inference, and Social Science: https://statmodeling.stat.columbia.edu/
- Andrew’s 2003 paper on visualization as model checks: “Exploratory Data Analysis for Complex Models”
- Jessica and Andrew’s follow-up article expanding on the idea of model checks for visualization research: “Designing for Interactive Exploratory Data Analysis Requires Theories of Graphical Inference”
- Andrew and Thomas’ paper on stories in social sciences: “When Do Stories Work? Evidence and Illustration in the Social Sciences”