Effects of experience-based frequency learning on posterior probability judgments
thesisposted on 24.05.2021, 13:08 by Bonnie A. Armstrong
Aging is associated with an increase in the frequency of medical screening tests. Bayesian inference is used to estimate posterior probabilities of medical tests such as positive or negative predictive values (PPVs or NPVs). Both laypeople and experts are typically poor at estimating PPVs and NPVs when relevant probabilities are communicated descriptively. Decision making research has revealed dissociations between described and experience-based judgments. This study examined the accuracy of posterior probability estimates of 80 younger and 81 older adults when statistical information was presented through description or experience. Results show that both younger and older adults can make more accurate posterior probability estimates if they experience probabilities compared to when probabilities are described as either natural frequencies or conditional probabilities. Results also indicate that most people prefer to rely on physicians to make their medical decisions regardless of how confident they are in their judgments of probabilities.