Does the Community Risk Scale Improve Probability Estimates? A. Bostrom, N. R. Brown, B. Hibbs, and R. Chen, Georgia Tech, University of Alberta, Centers for Disease Control and Prevention
Probabilities are critical in risk assessment and management, but are quantities that people generally have trouble understanding and using. While the proportion of adults in the U.S. who exhibit a grasp of probabilities has increased, it is estimated at only around 55%. Recent risk studies have illustrated that a noteworthy minority of survey respondents think that 1/100,000 is larger than 1/10,000. Many studies suggest that people have trouble with very small probabilities, and tend to provide compressed estimates of probabilities, at least for risks. A few recent studies have compared use of quantitative and qualitative probabilistic information in an effort to produce probabilistic information that is more useful, given the difficulties that professionals — such as doctors and engineers — as well as lay people, have with quantitative probabilistic information. These studies have met with some, but limited, success. Perhaps it is more relevant, however, that people prefer to receive precise (numerical) information about likelihoods (Olson and Budescu, 1997; but see also Wallsten et al. 1993). Thus there is a need for improved methods of communicating numerical probabilistic information. In this paper we extend Brown and Siegler’s (1993, 1996) metrics and mappings framework to probabilities, and test the effects of providing comparative probabilistic information (the community risk scale) on subjective estimates of probabilities in an experimental setting (N=66).
Support from Georgia Institute of Technology and the Centers for Disease Control and Prevention is gratefully acknowledged.
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