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Non-standard Errors
Albert J. Menkveld
Anna Dreber
Felix Holzmeister
Gerardo Ferrara
Magnus Johannesson
Simon Willi Jurkatis
Sebastian Neusüss
Jürgen Huber
Utz Weitzel
Michael Kirchler
Michael Razen
Christian Brownlees
David Abad Díaz
Edwin Baidoo
Gunther Capelle-Blancard
Oliver Linton
Stefan Palan
Andrea Schertler
Javier Gil Bazo
Menachem Abudy
Michael Frömmel
Erik Theissen
Tobias Adrian
Yacine Aït-Sahalia
Olivier Akmansoy
Jamie T. Alcock
Vitali Alexeev
Arash Aloosh
Livia Amato
Diego Amaya
James Joseph Angel
Alejandro T. Avetikian
Amadeus Bach
Gaetan Bakalli
Li Bao
Andrea Barbon
Oksana Bashchenko
Parampreet Christopher Bindra
Geir H. Bjønnes
出版
Research platform Empirical and Experimental Economics, University of Innsbruck
, 2021
URL
http://books.google.com.hk/books?id=vi_GzgEACAAJ&hl=&source=gbs_api
註釋
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.