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  The natural selection of bad science

Smaldino, P. E., & McElreath, R. (2016). The natural selection of bad science. Royal Society Open Science, 3(9): 160384. doi:10.1098/rsos.160384.

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Smaldino_Natural_RoySocOpSci_2016.pdf (Publisher version), 718KB
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Smaldino_Natural_RoySocOpSci_2016.pdf
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© 2016 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.
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Smaldino_Natural_RoySocOpSci_2016_Suppl.zip (Publisher version), 76KB
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© 2016 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.

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 Creators:
Smaldino, Paul E., Author
McElreath, Richard1, Author                 
Affiliations:
1Department of Human Behavior Ecology and Culture, Max Planck Institute for Evolutionary Anthropology, Max Planck Society, Deutscher Platz 6, 04103 Leipzig, DE, ou_2173689              

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Free keywords: metascience, cultural evolution, statistical power, replication, incentives, Campbell’s Law
 Abstract: Poor research design and data analysis encourage false-positive findings. Such poor methods persist despite perennial calls for improvement, suggesting that they result from something more than just misunderstanding. The persistence of poor methods results partly from incentives that favour them, leading to the natural selection of bad science. This dynamic requires no conscious strategizing—no deliberate cheating nor loafing—by scientists, only that publication is a principal factor for career advancement. Some normative methods of analysis have almost certainly been selected to further publication instead of discovery. In order to improve the culture of science, a shift must be made away from correcting misunderstandings and towards rewarding understanding. We support this argument with empirical evidence and computational modelling. We first present a 60-year meta-analysis of statistical power in the behavioural sciences and show that power has not improved despite repeated demonstrations of the necessity of increasing power. To demonstrate the logical consequences of structural incentives, we then present a dynamic model of scientific communities in which competing laboratories investigate novel or previously published hypotheses using culturally transmitted research methods. As in the real world, successful labs produce more ‘progeny,’ such that their methods are more often copied and their students are more likely to start labs of their own. Selection for high output leads to poorer methods and increasingly high false discovery rates. We additionally show that replication slows but does not stop the process of methodological deterioration. Improving the quality of research requires change at the institutional level.

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Language(s): eng - English
 Dates: 2016-09-21
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1098/rsos.160384
 Degree: -

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Title: Royal Society Open Science
Source Genre: Journal
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Pages: - Volume / Issue: 3 (9) Sequence Number: 160384 Start / End Page: - Identifier: ISSN: 2054-5703