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Non-Standard Errors

Menkveld, Albert J. (author)
Vrije Universiteit Amsterdam,Vrije Universiteit Amsterdam (NL)
Dreber Almenberg, Anna (author)
Handelshögskolan i Stockholm,Stockholm School of Economics
Holzmeister, Felix (author)
University of Innsbruck,University of Innsbruck (AT)
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Huber, Juergen (author)
University of Innsbruck,University of Innsbruck (AT)
Johannesson, Magnus (author)
Handelshögskolan i Stockholm,Stockholm School of Economics
Kirchler, Michael (author)
University of Innsbruck,University of Innsbruck (AT)
Neusüss, Sebastian (author)
Deutsche Börse
Razen, Michael (author)
University of Innsbruck,University of Innsbruck (AT)
Weitzel, Utz (author)
Vrije Universiteit Amsterdam,Vrije Universiteit Amsterdam (NL)
Wilhelmsson, Anders (author)
Lund University,Lunds universitet,Nationalekonomiska institutionen,Ekonomihögskolan,Department of Economics,Lund University School of Economics and Management, LUSEM
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 (creator_code:org_t)
et al 
2021
English 57 s.
Series: Working Papers
Related links:
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  • Other publication (other academic/artistic)
Abstract Subject headings
Close  
  • In statistics, samples are drawn from a population in a data generating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence generating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants.

Subject headings

NATURVETENSKAP  -- Matematik -- Sannolikhetsteori och statistik (hsv//swe)
NATURAL SCIENCES  -- Mathematics -- Probability Theory and Statistics (hsv//eng)

Keyword

Non-standard
errors
B26

Publication and Content Type

ovr (subject category)
vet (subject category)

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