The natural selection of bad science

Bibliographic Data

ID23329337
AuthorsP E Smaldino (0000-0002-7133-5620, University of California, Merced, corresponding author), Richard Mcelreath (0000-0002-0387-5377, Max Planck Institute for Evolutionary Anthropology)
Year2016
Volume3
Issue9
Pages160384
Publication date2016-09-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueRoyal Society Open Science (JOURNAL)
Journal identifiersISSN: 2054-5703 • E-ISSN: 2054-5703
PublisherThe Royal Society (PUBLISHER • GB)
DOI10.1098/rsos.160384
PMID27703703
OpenAlexW2416241581
LanguageEN
Citations received148
References cited87

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.

Argument (complex analysis) · Cheating · Data science · Economics · Epistemology · Incentive · Microeconomics · Normative · Political science · Popularity · Principal (computer security) · Process (computing) · Quality (philosophy) · Replication (statistics) · Selection (genetic algorithm) · Artificial Intelligence · Computer Science · Experimental Behavioral Economics Studies · Law · Psychology · scientometrics and bibliometrics research · Social Psychology · Species Distribution and Climate Change

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