Communicating uncertainty about facts, numbers and science

By A. M. van der Bles, S. van der Linden, A. Freeman, James Mitchell, A. Galvão, Lisa Zaval, David J. Spiegelhalter. 2019

358 citations recorded by the source corpus.

Uncertainty is an inherent part of knowledge, and yet in an era of contested expertise, many shy away from openly communicating their uncertainty about what they know, fearful of their audience's reaction. But what effect does communication of such epistemic uncertainty have? Empirical research is widely scattered across many disciplines. This interdisciplinary review structures and summarizes current practice and research across domains, combining a statistical and psychological perspective. This informs a framework for uncertainty communication in which we identify three objects of uncertainty—facts, numbers and science—and two levels of uncertainty: direct and indirect. An examination of current practices provides a scale of nine expressions of direct uncertainty. We discuss attempts to codify indirect uncertainty in terms of quality of the underlying evidence. We review the limited literature about the effects of communicating epistemic uncertainty on cognition, affect, trust and decision-making. While there is some evidence that communicating epistemic uncertainty does not necessarily affect audiences negatively, impact can vary between individuals and communication formats. Case studies in economic statistics and climate change illustrate our framework in action. We conclude with advice to guide both communicators and future researchers in this important but so far rather neglected field.

Subfields: General Decision Sciences, Sociology and Political Science

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In General Decision Sciences

1. Q. Liao
2. Jennifer Wortman Vaughan
3. B. Englich

In Sociology and Political Science

1. Supriyo Chakraborty
2. Q. Liao
3. Mani B. Srivastava
4. Lance M. Kaplan
5. Jennifer Wortman Vaughan
6. B. Englich

Citations in the corpus

• Rapid Trust Calibration through Interpretable and Uncertainty-Aware AI (2020, background): “Van der Bles et al.(22) surveyed epistemic uncertainty communication about facts, numbers, and science, but found no systematic studies of how epistemic uncertainty affects decision making (noting that many studies do not distinguish epistemic from aleatoric uncertainty).”
• No Replication, No Trust? How Low Replicability Influences Trust in Psychology (2019, background): “While findings regarding the effects of (scientific) uncertainty on audience reactions are rather inconclusive (for a review, see van der Bles et al., 2019), some studies suggest that nonscientists react negatively to scientific uncertainty.”
• AI Transparency in the Age of LLMs: A Human-Centered Research Roadmap (2023, background): “The psychology ), and what the effect is (e.g., on trust or behaviors), as well as taking into consideration of the characteristics of the receiver (Van Der Bles et al., 2019). s t For example, in our study on uncertainty in the context of code completion tools (Vasconcelos et al., 2023), by…”