Observing many researchers using the same data and hypothesis reveals a hidden universe of uncertainty

Abstract

This study explores how researchers’ analytical choices affect the reliability of scientific findings. Most discussions of reliability problems in science focus on systematic biases. We broaden the lens to emphasize the idiosyncrasy of conscious and unconscious decisions that researchers make during data analysis. We coordinated 161 researchers in 73 research teams and observed their research decisions as they used the same data to independently test the same prominent social science hypothesis: that greater immigration reduces support for social policies among the public. In this typical case of social science research, research teams reported both widely diverging numerical findings and substantive conclusions despite identical start conditions. Researchers’ expertise, prior beliefs, and expectations barely predict the wide variation in research outcomes. More than 95% of the total variance in numerical results remains unexplained even after qualitative coding of all identifiable decisions in each team’s workflow. This reveals a universe of uncertainty that remains hidden when considering a single study in isolation. The idiosyncratic nature of how researchers’ results and conclusions varied is a previously underappreciated explanation for why many scientific hypotheses remain contested. These results call for greater epistemic humility and clarity in reporting scientific findings.

Metadata

Item Type:	Article
Authors/Creators:	Breznau, N Rinke, EM https://orcid.org/0000-0002-5330-7634 Wuttke, A Nguyen, HHV Adem, M Adriaans, J Alvarez-Benjumea, A Andersen, HK Auer, D Azevedo, F Bahnsen, O Balzer, D Bauer, G Bauer, PC Baumann, M Baute, S Benoit, V Bernauer, J Berning, C Berthold, A Bethke, FS Biegert, T Blinzler, K Blumenberg, JN Bobzien, L Bohman, A Bol, T Bostic, A Brzozowska, Z Burgdorf, K Burger, K Busch, KB Carlos-Castillo, J Chan, N Christmann, P Connelly, R Czymara, CS Damian, E Ecker, A Edelmann, A Eger, MA Ellerbrock, S Forke, A Forster, A Gaasendam, C Gavras, K Gayle, V Gessler, T Gnambs, T Godefroidt, A Grömping, M Groß, M Gruber, S Gummer, T Hadjar, A Heisig, JP Hellmeier, S Heyne, S Hirsch, M Hjerm, M Hochman, O Hövermann, A Hunger, S Hunkler, C Huth, N Ignácz, ZS Jacobs, L Jacobsen, J Jaeger, B Jungkunz, S Jungmann, N Kauff, M Kleinert, M Klinger, J Kolb, J-P Kołczyńska, M Kuk, J Kunißen, K Kurti Sinatra, D Langenkamp, A Lersch, PM Löbel, L-M Lutscher, P Mader, M Madia, JE Malancu, N Maldonado, L Marahrens, H Martin, N Martinez, P Mayerl, J Mayorga, OJ McManus, P McWagner, K Meeusen, C Meierrieks, D Mellon, J Merhout, F Merk, S Meyer, D Micheli, L Mijs, J Moya, C Neunhoeffer, M Nüst, D Nygård, O Ochsenfeld, F Otte, G Pechenkina, AO Prosser, C Raes, L Ralston, K Ramos, MR Roets, A Rogers, J Ropers, G Samuel, R Sand, G Schachter, A Schaeffer, M Schieferdecker, D Schlueter, E Schmidt, R Schmidt, KM Schmidt-Catran, A Schmiedeberg, C Schneider, J Schoonvelde, M Schulte-Cloos, J Schumann, S Schunck, R Schupp, J Seuring, J Silber, H Sleegers, W Sonntag, N Staudt, A Steiber, N Steiner, N Sternberg, S Stiers, D Stojmenovska, D Storz, N Striessnig, E Stroppe, A-K Teltemann, J Tibajev, A Tung, B Vagni, G Van Assche, J van der Linden, M van der Noll, J Van Hootegem, A Vogtenhuber, S Voicu, B Wagemans, F Wehl, N Werner, H Wiernik, BM Winter, F Wolf, C Yamada, Y Zhang, N Ziller, C Zins, S Żółtak, T
Copyright, Publisher and Additional Information:	© 2022 the Author(s). Published by PNAS. This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY).
Keywords:	metascience; many analysts; researcher degrees of freedom; analytical flexibility; immigration and policy preferences
Dates:	Published: 1 November 2022 Published (online): 28 October 2022 Accepted: 22 August 2022
Institution:	The University of Leeds
Academic Units:	The University of Leeds > Faculty of Education, Social Sciences and Law (Leeds) > School of Politics & International Studies (POLIS) (Leeds)
Depositing User:	Symplectic Publications
Date Deposited:	01 Nov 2022 16:11
Last Modified:	01 Nov 2022 16:11
Status:	Published
Publisher:	National Academy of Sciences
Identification Number:	10.1073/pnas.2203150119
Open Archives Initiative ID (OAI ID):	oai:eprints.whiterose.ac.uk:192743

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Observing many researchers using the same data and hypothesis reveals a hidden universe of uncertainty

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