Communicating uncertainty about facts, numbers and science

Abstract

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.

Framing science: How opioid research is presented in online news media

“…In doing so, this study builds on the limited research investigating the portrayal of scientific evidence in the news (Zehr, 2000;Antilla, 2005;Ruhrmann et al, 2015;Guenther et al, 2019), and, more specifically, the portrayal of scientific uncertainty in such coverage (Ashe, 2013;Dan and Raupp, 2018;van der Bles et al, 2019). Previous studies have revealed a general tendency for news media to gloss over uncertainties and unknowns when covering science and health issues (Jung Oh et al, 2012;Hove et al, 2015;Schneider, 2016;Dan and Raupp, 2018).…”

“…The implications of representing science as uncertain thus appear to depend on the nature of the topic itself (i.e., how controversial it is, how well it has been studied), as well as the larger communication context (e.g., audience knowledge and beliefs, format, medium, etc. ; van der Bles et al, 2019). As a result of the conflicting evidence, science communication scholars remain divided about whether highlighting scientific uncertainties in news coverage is beneficial or harmful to the public perception and understanding of science (Corbett and Durfee, 2004;Fischhoff, 2013;Schneider, 2016;Gustafson and Rice, 2019), and communicators often struggle with how best to portray scientific uncertainty when sharing scientific findings with lay audiences (Peters and Dunwoody, 2016).…”

Learnings for Health Economics from the Early Stages of the COVID-19 Pandemic

“…If the IPCC provides estimates or statements, they use a standardised language: an outcome stated to be 'very likely' in all cases will mean it is predicted to happen with 90-100% probability. The IPCC work then goes further by discussing the degree of agreement in the evidence (low, medium or high) and the quality of evidence (limited, medium or robust) [7]. Standardisation of an approach to describing uncertainty in HE would avoid misinterpretation, both within and outside our discipline, using a common currency.…”

Development and Validation of the TRansparent Uncertainty ASsessmenT (TRUST) Tool for Assessing Uncertainties in Health Economic Decision Models

“…We developed a first version of the tool based on the literature on uncertainty and risk frameworks in HTA and risk assessment disciplines [14,16,18,[27][28][29]. Relevant literature was identified in a review of the literature on uncertainty frameworks.…”

“…To the best of our knowledge, no tool for systematically identifying uncertainty about the quality of evidence used in models is currently available. Regarding (2), uncertainty is most completely characterised with a full explicit probability distribution [4,5,18,19]. In theory, it may be possible to represent all uncertainty in this way, for example using methods to express expert opinion [20,21] and/or structural uncertainty [22][23][24].…”