We often turn to data to help us make sense of an uncertain world. However, the uncertainty in our data is often esoteric, complex, or counter-intuitive. It can be challenging to present this uncertainty, especially to audiences without backgrounds in statistics.
Charts, graphs, and other visualizations of data address this issue by making people into “visual statisticians:” we can estimate statistical properties through visual inspection. However, just as statistical measures can be subject to bias, visualizations can also introduce bias.
In this talk, I show how designers can intervene to create new visualizations that correct these biases, and improve the judgments of visual statisticians. From this perspective of designing for de-biasing, I focus on two common visualizations: error bars and thematic maps. I present visual alternatives for error bars that avoid “within-the-bar” bias while also promoting statistically grounded comparisons between means. I also present “Surprise Maps,” a technique for thematic maps that relies on Bayesian reasoning to highlight interesting regions that might otherwise be hidden in traditional maps. I conclude with a discussion of remaining challenges for visual de-biasing, and how we might use visualizations to encourage better, data-driven decision-making.
Michael Correll is a postdoctoral research associate at the Interactive Data Lab at University of Washington. He received his PhD. in Computer Sciences from the University of Wisconsin-Madison in 2015. His research focuses on information visualization, and more specifically on ways to present statistical information to general audiences. His other interests include graphical perception, visual rhetoric, and the digital humanities.