Menkveld, Albert; Dreber, Anna; Holzmeister, Felix; Huber, Juergen; Johannesson, Magnus; Kirchler, Michael; Neusus, Sebastian; Razen, Michael; Weitzel, Utz; Abad-Diaz, David; Abudy, Menachem; Adrian, Tobias; Ait-Sahalia, Yacine; Akmansoy, Olivier; Alcock, Jamie T.; Alexeev, Vitali; Aloosh, Arash; Amato, Livia; Amaya, Diego; Angel, James J.; Avetikian, Alejandro T.; Bach, Amadeus; Baidoo, Edwin; Bakalli, Gaetan; Bao, Li; Bardon, Andrea; Bashchenko, Oksana; Bindra, Parampreet C.; Bjønnes, Geir Høidal; Black, Jeffrey R.; Black, Bernard S.; Bogoev, Dimitar; Correa, Santiago Bohorquez; Bondarenko, Oleg; Bos, Charles S.; Bosch-Rosa, Ciril; Bouri, Elie; Brownlees, Christian; Calamia, Anna; Cao, Viet Nga; Capelle-Blancard, Gunther; Romero, Laura M. Capera; Caporin, Massimiliano; Carrion, Allen; Caskurlu, Tolga; Chakrabarty, Bidisha; Chen, Jian; Chernov, Mikhail; Cheung, William; ter Ellen, Saskia; Ødegaard, Bernt Arne; Longarela, Inaki Rodriguez; Wika, Hans C.; Yuferova, Darya (Journal article; Tidsskriftartikkel; Peer reviewed, 2024-04-17)
In statistics, samples are drawn from a population in a data‐generating process (DGP). Standard errors measure the uncertainty in 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—nonstandard errors (NSEs). We study NSEs by letting 164 teams test the same ...
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