Probability Distributions of Common Repeated Events are Misestimated

65 Pages Posted: 18 Feb 2016

See all articles by Oleg Urminsky

Oleg Urminsky

University of Chicago - Booth School of Business

Date Written: 2015

Abstract

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.

Suggested Citation

Urminsky, Oleg, Probability Distributions of Common Repeated Events are Misestimated (2015). Available at SSRN: https://ssrn.com/abstract=2733345 or http://dx.doi.org/10.2139/ssrn.2733345

Oleg Urminsky (Contact Author)

University of Chicago - Booth School of Business ( email )

5807 S. Woodlawn Avenue
Chicago, IL 60637
United States

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