Probability Distributions of Common Repeated Events are Misestimated
65 Pages Posted: 18 Feb 2016
Date Written: 2015
Accurately estimating the probability distribution arising from repeated events with known probabilities, such as the number of heads in ten coin flips, represents a simple aptitude necessary for Bayesian updating and optimal decisions in the face of future uncertainty. Across elicitation methods and decision scenarios, people express beliefs that are systematically biased relative to the actual distribution. Participant beliefs reflect a “wizard-hat” shaped distribution, over-estimating the tails and under-estimating the shoulders of the distribution, relative to the actual bell-curve shape. While experts are relatively more accurate than novices, both show significant bias. The findings challenge an emerging view that the human brain is adept at optimal statistical processing.
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