Attempts To Debias Wishful Thinking In The 2016 U.S. Presidential Election

ASU Author/Contributor (non-ASU co-authors, if there are any, appear on document)
Victor L. Norris III (Creator)
Appalachian State University (ASU )
Web Site:
Andrew R. Smith

Abstract: People’s preferences for an outcome can influence how they perceive the likelihood of that outcome over others. For example, a football fan may overestimate the likelihood of his favorite team winning an upcoming game. This phenomenon is referred to as wishful thinking. Previous research has examined many factors that could potentially decrease wishful thinking, and most of these attempts have been unsuccessful. For example, even when given an incentive to make an accurate prediction, participant’s preferences still influenced their predictions. The current study expands previous literature by examining how additional and accurate information influences wishful thinking. I predicted that providing participants with additional information would decrease the amount of wishful thinking they demonstrate in predicting the outcome of the U.S. 2016 Presidential Election. Among other questions, participants were asked to indicate which candidate they preferred to win the election. Then, participants were either given no information, current polling information, or election expert opinions. Participants then predicted who they thought would win the election. Inconsistent with my hypothesis, providing additional information did not decrease wishful thinking. This study is consistent with other failed attempts to decrease wishful thinking and highlights how influential people’s preferences can be to the judgments they make.

Additional Information

Honors Project
Norris, V. (2017). "Attempts To Debias Wishful Thinking In The 2016 U.S. Presidential Election." Unpublished Honors Thesis. Appalachian State University, Boone, NC.
Language: English
Date: 2017
Wishful thinking, Desirability bias, 2016 Presidential Election, Wishful thinking predictions, Decrease wishful thinking

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