Menkveld, Albert J.
[Author];
Dreber, Anna
[Author];
Holzmeister, Felix
[Author];
Huber, Jürgen
[Author];
Johannesson, Magnus
[Author];
Kirchler, Michael
[Author];
Neusüss, Sebastian
[Author];
Razen, Michael
[Author];
Weitzel, Utz
[Author]
You can manage bookmarks using lists, please log in to your user account for this.
Media type:
Report;
E-Book
Title:
Non-standard errors
Contributor:
Menkveld, Albert J.
[Author];
Dreber, Anna
[Author];
Holzmeister, Felix
[Author];
Huber, Jürgen
[Author];
Johannesson, Magnus
[Author];
Kirchler, Michael
[Author];
Neusüss, Sebastian
[Author];
Razen, Michael
[Author];
Weitzel, Utz
[Author]
Published:
Frankfurt a. M.: Leibniz Institute for Financial Research SAFE, 2021
Footnote:
Diese Datenquelle enthält auch Bestandsnachweise, die nicht zu einem Volltext führen.
Description:
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.