Communicating scientific uncertainty

Abstract

All science has uncertainty. Unless that uncertainty is communicated effectively, decision makers may put too much or too little faith in it. The information that needs to be communicated depends on the decisions that people face. Are they (i) looking for a signal (e.g., whether to evacuate before a hurricane), (ii) choosing among fixed options (e.g., which medical treatment is best), or (iii) learning to create options (e.g., how to regulate nanotechnology)? We examine these three classes of decisions in terms of how to characterize, assess, and convey the uncertainties relevant to each. We then offer a protocol for summarizing the many possible sources of uncertainty in standard terms, designed to impose a minimal burden on scientists, while gradually educating those whose decisions depend on their work. Its goals are better decisions, better science, and better support for science.

Keywords: expert elicitation; expert judgment; mental models; risk; science communication.

Fig. 1.

Signal detection theory in evaluating decisions to transfer ER patients. Emergency physicians provided written recommendations for the next steps in treating patients depicted in detailed profiles drawn from actual records. Higher values for the decisional threshold indicate more cautious decisions (β). Higher values for perceptual sensitivity indicate better discrimination ability (d′). American College of Surgeons–Committee on Trauma (8) guidelines were applied to the profiles to identify the appropriate decision. (Reproduced with permission from ref. .)

Fig. 2.

Methodological flaws in field trials of interventions for reducing home electricity consumption. (Reproduced with permission from ref. .)

Fig. 3.

Influence diagram showing the expertise needed for systematic assessment of the uncertainty in responses to drinking water contamination. (Reproduced with permission from ref. .)