In this report, we consider one such formal system: the National Football League (NFL). In such a system with zero-sum outcomes, optimism bias is conclusively shown when people’s predictions are collectively inconsistent. Ideally, optimism bias would be evaluated within a closed formal system to establish with certainty when the bias occurs, the extent of it, and the associated environmental predictors. One research challenge is conclusively evaluating whether optimism bias exists. One person avoiding cancer does not necessitate that another person develops cancer. However, in all these examples, one’s own outcome is not zero sum with those of others. At its heart, optimism bias involves believing that one will fare better than average. For example, there are numerous ways people could construe being a better than average driver. ![]() ![]() Furthermore, what it means to be “better” than average is malleable, multidimensional, and open to interpretation in many cases. For example, almost every human has more arms and legs than the average. Skewed distributions can also create issues when people fail to appreciate the difference between the mean and the median. Testing for optimism bias involves relating an environmental statistic (e.g., cancer rate) to the participant sample in the study and this sample may not be drawn from the same distribution that generated the statistic. For example, when people are optimistic about their cancer prospects, perhaps they are accessing additional information about their family history or believe medical science will advance in the coming years. Unfortunately, interpreting results in real-world domains can be non-trivial to the point where some question whether there is an optimism bias. ![]() On the other hand, optimism bias can have negative consequences, such as not seeking needed medical care because of underestimated risk. Optimism bias may be so prevalent because it is associated with improved health outcomes and workplace performance, whereas realistic expectations are associated with depression. For example, people have overly rosy forecasts of their prospects in regards to traffic accidents, cancer risk, and work-place safety. Optimism bias refers to people's tendency to overestimate the probability of experiencing positive outcomes and underestimate the probability of experiencing negative outcomes. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Ĭompeting interests: The authors have declared that no competing interests exist. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are creditedĭata Availability: Raw data are available online at the Open Science Foundation, osf.io/3xrfa.įunding: This work was supported by the Leverhulme Trust grant RPG-2014-075 ( and Wellcome Trust Senior Investigator Award WT106931MA ( to BCL. Received: JAccepted: AugPublished: September 9, 2015Ĭopyright: © 2015 Love et al. PLoS ONE 10(9):Įditor: Edward Vul, University of California, San Diego, UNITED STATES ![]() Citation: Love BC, Kopeć Ł, Guest O (2015) Optimism Bias in Fans and Sports Reporters.
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