Jessica Hullman has been writing about the visualisation of uncertainty in a Scientific American article (link). Her mission is “to help more people make sense of complex information, and in particular to reason about data under uncertainty” (link).
Her Scientific American article notes:
Uncertainty pervades the data that scientists and all kinds of organizations use to inform decisions. Visual depictions of information can help clarify the uncertainty—or compound confusion. Ideally, visualizations help us make judgments, analytically and emotionally, about the probability of different outcomes. Abundant evidence on human reasoning suggests, however, that when people are asked to make judgments involving probability, they often discount uncertainty. As society increasingly relies on data, graphics designers are grappling with how best to show uncertainty clearly.
She and Matthew Key expand their discussion of uncertainty in Multiple Views (a blog about visualisation research, for anyone, by the people who do it).
Jessica and Matthew provided an introduction to uncertainty visualisation earlier this year (link). They observe “when we talk about visualizing uncertainty, though, we usually mean visualizing information about different values the data could plausibly be”.
They suggest “if the goal is simply to convey a coarse sense of uncertainty, a gradient plot might suffice. If the goal is for the user to be able to compare the relative amounts of probability density to make decisions, a density plot is more appropriate” (link).
I think their work is very important to us as analysts. We are encouraged to model performance and to share these models. Jessica and Matthew have started an important conversation about this and have invited us to think how probability and uncertainty fit into our practice.