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Andreea, Valente, Giorgio, Ness, Robert A.Van, Vasquez, Aurelio, Verousis, Thanos, Verwijmeren, Patrick, Vilhelmsson, Anders, Vilkov, Grigory, Vladimirov, Vladimir, Vogel, Sebastian, Voigt, Stefan, Wagner, Wolf, Walther, Thomas, Weiss, Patrick, Wel, Michel Van Der, Werner, Ingrid M., Westerholm, P. Joakim, Westheide, Christian, Wika, Hans C., Wipplinger, Evert, Wolf, Michael, Wolff, Christian C.P., Wolk, Leonard, Wong, Wing Keung, Wrampelmeyer, Jan, Wu, Zhen Xing, Xia, Shuo, Xiu, Dacheng, Xu, Ke, Xu, Caihong, Yadav, Pradeep K., Yagüe, José, Yan, Cheng, Yang, Antti, Yoo, Woongsun, Yu, Wenjia, Yu, Yihe, Yu, Shihao, Yueshen, Bart Z., Yuferova, Darya, Zamojski, Marcin, Zareei, Abalfazl, Zeisberger, Stefan M., Zhang, Lu, Zhang, S. Sarah, Zhang, Xiaoyu, Zhao, Lu, Zhong, Zhuo, Zhou, Z. Ivy, Zhou, Chen, Zhu, Xingyu S., Zoican, Marius and Zwinkels, Remco (2024) Nonstandard errors. Journal of Finance, 79 (3). 2339 - 2390. ISSN 0022-1082
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Abstract
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 hypotheses on the same data. NSEs turn out to be sizable, but smaller for more reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.
Item Type: | Article |
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Official URL: | https://onlinelibrary.wiley.com/journal/15406261 |
Additional Information: | © 2024 The Authors |
Divisions: | Systemic Risk Centre |
Subjects: | H Social Sciences > HA Statistics H Social Sciences > HG Finance |
Date Deposited: | 10 May 2024 08:45 |
Last Modified: | 17 Nov 2024 04:54 |
URI: | http://eprints.lse.ac.uk/id/eprint/123002 |
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