Non-Standard Errors

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 resea...

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Main Authors: Johannesson, Magnus, Alcock, Jamie, Alexeev, Vitali, Aloosh, Arash, Barbon, Andrea, Brownlees, Christian T., Cao, Viet Nga, Capelle-Blancard, Gunther, Chernov, Mikhail, Chincarini, Ludwig B., Chordia, Tarun, Clapham, Benjamin, Deku, Solomon, Dimpfl, Thomas, Ellen, Saskia ter, Farrell, Michael, Flori, Andrea, Franus, Tatiana, Fu, Servanna, Füllbrunn, Sascha, Gan, Baoqing, Gomez, Thomas, Güçbilmez, Ufuk, Grammig, Joachim, Gregoire, Vincent, Hagströmer, Björn, Harris, Jeffrey H., Harris, Lawrence, Hartmann, Simon, He, Xuezhong, Heath, Davidson, Hjalmarsson, Erik, Ivashchenko, Alexey, Iyer, Subramanian R., Jahanshahloo, Hossein, Jurkatis, Simon, Kaeck, Andreas, Kassner, Bernhard, Kearney, Fearghal, Kervel, Vincent van, Klos, Alexander, Kozhan, Roman, Lam, FY Eric C, Leippold, Markus, Li, Yijie, Llorente, Guillermo, Mazzola, Francesco, Meloso, Debrah, Mihet, Roxana, Neumeier, Christian, Nielsson, Ulf, Nolte, Sven, Norden, Lars L., Östberg, Per, Park, Andreas, Patel, Vinay, Pelizzon, Loriana, Press, Oliver-Alexander, Putniņš, Tālis J., Renault, Thomas, Renjie, Rex Wang, Renò, Roberto, Rognone, Lavinia, Rosu, Ioanid, Rudolf, Nicolas, Rush, Stephen, Rzayev, Khaladdin, Rzeźnik, Aleksandra, Sankaran, Harikumar, Scaillet, O., Schertler, Andrea, Seasholes, Mark S., Shui, Jessica, Sojli, Elvira, Neusüss, Sebastian, Szaszi, Barnabas, Taylor, Nicholas, Tham, Wing Wah, Theissen, Erik, Trolle, Anders B., Vilkov, Grigory, Werner, Ingrid M., Wolk, Leonard, Wong, Wing Keung, Xu, Ke, Yadav, Pradeep K., Yagüe, José, Zamojski, Marcin, Zareei, Abalfazl, Zhang, Xiaoyu, Zhong, Zhuo, Zhu, Xingyu Sonya, Chen, Jian, Gilder, Dudley, Bogoev, Dimitar, Wika, Hans C., Zhao, Lu, Bao, Li, Prokopczuk, Prokopczuk, Avetikian, Alejandro
Format: Web Resource
Language:English
Published: Blackwell 2024
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Summary: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: Non-standard errors NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.
ISSN:1540-6261
0022-1082