In this thesis, we hypothesize that data visualization users are subject to systematic errors, or cognitive biases, in decision-making under uncertainty. Based on research from psychology, behavioral economics, and cognitive science, we design five …
From a once-in-a-century pandemic to volatile swings in stock markets to turbulent political elections, uncertainty is all around us. Yet why do so many data analysts, data scientists and data journalists tend to ignore uncertainty? Or worse, can bad …
Understanding correlation judgement is important to designing effective visualizations of bivariate data. Prior work on correlation perception has not considered how factors including prior beliefs and uncertainty representation impact such …
We propose a visualization technique, Du Bois wrapped bar chart, inspired by work of W.E.B Du Bois. Du Bois wrapped bar charts enable better large-to-small bar comparison by wrapping large bars over a certain threshold. We first present two …