 Non-Standard ErrorsAlbert Menkveld,
Anna Dreber,
Felix Holzmeister,
Juergen Huber,
Magnus Johannesson,
Michael Kirchler,
Sebastian Neusüss,
Michael Razen (),
Utz Weitzel,
Gunther Capelle-Blancard,
David Abad-Díaz,
Menachem Abudy,
Tobias Adrian,
Yacine Ait-Sahalia,
Olivier Akmansoy,
Jamie Alcock,
Vitali Alexeev,
Arash Aloosh,
Livia Amato,
Diego Amaya,
James Angel,
Alejandro Avetikian,
Amadeus Bach,
Edwin Baidoo,
Gaetan Bakalli,
Bao Li,
Andrea Barbon,
Oksana Bashchenko,
Parampreet Bindra,
Geir Bjønnes,
Jeffrey Black,
Bernard Black,
Dimitar Bogoev,
Santiago Bohorquez Correa,
Oleg Bondarenko,
Charles Bos,
Ciril Bosch-Rosa,
Elie Bouri,
Christian Brownlees,
Anna Calamia,
Nga Cao,
Laura Capera Romero,
Massimiliano Caporin,
Allen Carrion,
Tolga Caskurlu,
Bidisha Chakrabarty,
Jian Chen,
Mikhail Chernov,
William Cheung,
Ludwig Chincarini,
Tarun Chordia,
Nikolai Sheung-Chi Chow,
Benjamin Clapham,
Jean-Edouard Colliard,
Carole Comerton-Forde,
Edward Curran,
Thong Dao,
Wale Dare,
Ryan Davies,
Riccardo de Blasis,
Gianluca de Nard,
Fany Declerck,
Oleg Deev,
Hans Degryse,
Solomon Deku,
Christophe Desagre,
Mathijs van Dijk,
Chukwuma Dim,
Thomas Dimpfl,
Yun Dong,
Philip Drummond,
Tom Dudda,
Teodor Duevski,
Ariadna Dumitrescu (),
Teodor Dyakov,
Anne Dyhrberg,
Michał Dzieliński,
Asli Eksi,
Izidin El Kalak,
Saskia ter Ellen,
Nicolas Eugster,
Martin Evans,
Michael Farrell,
Ester Felez-Vinas,
Gerardo Ferrara,
El Mehdi Ferrouhi,
Andrea Flori,
Jonathan Fluharty,
Sean Foley,
Kingsley Fong,
Thierry Foucault,
Tatiana Franus,
Francesco Franzoni,
Bart Frijns,
Michael Frömmel,
Servanna Fu,
Sascha Füllbrunn (),
Baoqing Gan,
Ge Gao,
Thomas Gehrig,
Roland Gemayel,
Dirk Gerritsen,
Javier Gil-Bazo,
Dudley Gilder and
Lawrence Glosten
Post-Print from HAL Abstract: 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. Keywords: non-standard errors; multi-analyst approach; liquidity (search for similar items in EconPapers) Published in 2021 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:halshs-03500882 Access Statistics for this paper More papers in Post-Print from HAL |
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