The utility of theories in intuitive statistics: The robustness of theory-based judgments

Jack C. Wright, Gregory L. Murphy

Research output: Contribution to journalArticlepeer-review


Research on human judgment demonstrates that people's theories often bias their evaluation of evidence and suggests that people might be more accurate if they were unbiased by prior beliefs. In 2 studies using the covariation estimation problem and the t-test problem, judgments made by Ss who had potentially biasing prior information about data were compared to those made by Ss who were not biased by prior information. 265 undergraduates served as Ss in Study 1; 201 undergraduates were Ss in Study 2. The quality of the data was varied to present Ss with data that were either well-behaved or contaminated with outliers. In both studies, Ss' judgments approximated robust statistical measures rather than the conventional measures typically used as normative criteria. The usual biasing effects of prior beliefs were found, along with an advantage for Ss who had prior theories--even incorrect ones--over Ss who were completely "objective." Potentially biasing beliefs both enhanced Ss' sensitivity to the bulk of the data and reduced the influence atypical scores had on their estimates. Evidence is provided that this robustness results from the fact that prior theories make judgment problems more meaningful. (40 ref) (PsycINFO Database Record (c) 2006 APA, all rights reserved).

Original languageEnglish (US)
Pages (from-to)301-322
Number of pages22
JournalJournal of Experimental Psychology: General
Issue number2
StatePublished - Jun 1984


  • implicit theories &
  • prior information, bias in judgments of statistical data, college students

ASJC Scopus subject areas

  • Experimental and Cognitive Psychology
  • General Psychology
  • Developmental Neuroscience


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