This page doesn't get updated often, so see my latest publications or what I'm blogging about for a better read on my current research interests.

Uncertainty Representations that Work

Many people, including analysts, find uncertainty and probability difficult to reason about when working with data. The use of null hypothesis significance testing is increasingly criticized; however we lack good representations of uncertainty that can provide analysts and readers of scientific literature with ''cognitive evidence'' for understanding variation, reliability, and related statistical concepts. Most uncertainty visualizations are static and present probability continuously, as in density plots or CDFs, or using difficult to understand constructs like confidence intervals. I have pioneered the use of hypothetical outcome plots (HOPs): animated visualizations in which each frame presents a draw from a distribution describing uncertainty in a parameter. Perceptual psychology shows that a frequency encoding does not require conscious effort to interpret, while judgment and decision making demonstrates how framing probabilities as frequency (e.g., 3 out of 10 rather than 30%) eases interpretation for novices and experts alike. Through controlled empirical studies of inference and perceptual decision making, I have shown how HOPs vastly improve multivariate probability estimation and make people more sensitive to the underlying trend in noisy data without requiring the same levels of training as more conventional static plots.

In some settings, animated plots are not feasible. I have also worked to develop non-animated discrete outcome encodings of uncertainty information, such as a quantile dotplot: a discrete outcome representation of a probability density function. Collaborators and I have shown how this encoding better supports everyday decisions and reasoning, including when to leave for the bus and judgments about how reliable the effect reported in media coverage of a scientific study is.

animated visualization of uncertaintyinteractive quantile dotplots

Representative Contributions

Integrating Prior Knowledge in Visualization Interaction

Users' prior knowledge undoubtedly impacts the con- clusions they draw from data. However, visualization design and evaluation techniques rarely account for prior beliefs. My research shows how enabling users to articulate their predictions of data via graphical elicitation before they see the observed data in a visualization can improve their ability to understand and recall the data. Properties of the alignment between a person's prior beliefs, the data, and others' (visualized) beliefs can be used to predict how people will update their beliefs. I am currently developing Bayesian models of visualization cognition. By comparing posterior beliefs about a visualized phenomena to normative beliefs under Bayesian inference, a Bayesian approach more precisely explains why some visualizations designs perform poorly with greater precision than existing evaluations, such as by quantifying discrepancies in actual and perceived data sample size and indicating where people may be reasoning with a few salient samples from their prior experiences rather than full subjective uncertainty distributions.

Representative Contributions