Authors
Anna Dreber, Albert J Menkveld, Felix Holzmeister, Magnus Johannesson, Juergen Huber, Michael Kirchler, Michael Razen, Utz Weitzel, David Abad, Menachem Meni Abudy, Tobias Adrian, Yacine Ait-Sahalia, Olivier Akmansoy, Jamie Alcock, Vitali Alexeev, Arash Aloosh, Livia Amato, Diego Amaya, James Angel, Amadeus Bach, Edwin Baidoo, Gaetan Bakalli, Andrea Barbon, Oksana Bashchenko, Parampreet Christopher Bindra, Geir Hoidal Bjonnes, Jeff Black, Bernard S Black, Santiago Bohorquez, Oleg Bondarenko, Charles S Bos, Ciril Bosch-Rosa, Elie Bouri, Christian T Brownlees, Anna Calamia, Viet Nga Cao, Gunther Capelle-Blancard, Laura Capera, Massimiliano Caporin, Allen Carrion, Tolga Caskurlu, Bidisha Chakrabarty, Mikhail Chernov, William M Cheung, Ludwig B Chincarini, Tarun Chordia, Sheung Chi Chow, Benjamin Clapham, Jean-Edouard Colliard, Carole Comerton-Forde, Edward Curran, Thong Dao, Wale Dare, Ryan J Davies, Riccardo De Blasis, De Nard
Publication date
2023
Journal
The Journal of Finance
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.
Total citations
Scholar articles
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