# 407 | ResearchBox

ResearchBox # 407 - 'Outlier....s'


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Study 1


  Study 1 - AsPredicted #73364.pdf



  outliers_-_followup.qsf


  


  outfollow1.csv


  outfollow.R


Study 2
  


  trainsdata.csv


  


  bookstoredata.csv


  analyses.R


Supplement


  Appendix.docx



  Supplemental Materials-outlier.docx


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BOX INFORMATION

SUPPLEMENTARY FILES FOR
Dannals JE, Oppenheimer DM. (2022) 'How people deal with … outliers'. Journal of Behavioral Decision Making. .
doi: 10.1002/bdm.2303

LICENSE FOR USE
All content posted to ResearchBox is under a CC By 4.0 License (all use is allowed as long as authorship of the content is attributed). When using content from ResearchBox please cite the original work, and provide a link to the URL for this box (https://researchbox.org/407).

BOX PUBLIC SINCE
September 26, 2022   (files may not be changed, deleted, or added)

BOX CREATORS
Jennifer Dannals (jennifer.dannals@yale.edu)
Daniel Oppenheimer (doppenh1@andrew.cmu.edu)

ABSTRACT
People regularly make sense of distributions that are complicated by noise. How do individuals determine whether an outlying observation should be incorporated into one’s understanding of the true distribution of the population or considered a fluke that ought to be disregarded? In a simple prediction task, we examine how individuals incorporate outliers and compare their behavior to various prescriptive models (e.g. averaging, tests of discordancy). We find that, on average, individuals do discount outlying values and that their outlier detection strategies approximate approaches that statisticians have recommended for Gaussian distributions, even when the observed distributions are not Gaussian. However there are notable differences in treatment of outliers across individuals.