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Related Concept Videos

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The confirmation bias is the tendency to focus on information that confirms our existing beliefs and ignore information that is inconsistent with our expectations. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Have you ever fallen prey to the confirmation bias, either as the source or target of such bias?
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While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actually causing the systematic movement in our variables of interest. For instance, as sales in ice cream increase, so does the overall rate of crime. Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone?
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When we hold a stereotype about a person, we have expectations that he or she will fulfill that stereotype. A self-fulfilling prophecy is an expectation held by a person that alters his or her behavior in a way that tends to make it true. When we hold stereotypes about a person, we tend to treat the person according to our expectations. This treatment can influence the person to act according to our stereotypic expectations, thus confirming our stereotypic beliefs. Research by Rosenthal and...
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Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
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"Stop the Count!"-How Reporting Partial Election Results Fuels Beliefs in Election Fraud.

André Vaz1, Moritz Ingendahl1, André Mata2

  • 1Faculty of Psychology, Ruhr University Bochum.

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Summary

Reporting partial vote counts can create a cumulative redundancy bias (CRB), leading people to suspect election fraud. This bias affects perceptions even when the eventual winner has a late lead, persisting across studies.

Keywords:
2020 U.S. presidential electioncognitive biascumulative redundancy biaselectoral fraudvote reporting

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Area of Science:

  • Psychology
  • Political Science
  • Communication Studies

Background:

  • Beliefs in election fraud, exemplified by the 2020 U.S. presidential election, can be influenced by cognitive biases.
  • The cumulative redundancy bias (CRB) may skew perceptions of election outcomes based on the order of reported vote counts.

Purpose of the Study:

  • To investigate how reporting partial vote counts influences perceptions of election legitimacy.
  • To examine the role of the cumulative redundancy bias (CRB) in fueling suspicions of election fraud.

Main Methods:

  • Seven studies were conducted using simulated and real-world election data.
  • Participants rated election legitimacy and suspected fraud based on partial vote count reporting.
  • The influence of the cumulative redundancy bias (CRB) was assessed across various conditions, including interventions and partisanship.

Main Results:

  • Participants consistently favored early leaders and were more likely to suspect fraud when the eventual winner had a late lead.
  • These effects were observed in simulated elections and real-world data from the 2020 Georgia election.
  • Fraud suspicions emerged early in the vote count and persisted despite interventions; partisanship did not eliminate the bias.

Conclusions:

  • Sequential reporting of vote counts can amplify false perceptions of election fraud due to the cumulative redundancy bias (CRB).
  • Revising how election results are communicated may mitigate these biased perceptions and enhance trust in election legitimacy.