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The natural selection of bad science

2016/05/31 by Paul E. Smaldino, Richard McElreath · 13 voices · 915 citations
Decision Sciences · Environmental Science · Mathematics · Physics and Astronomy · Psychology · Social Sciences · #Argument (complex analysis) #Artificial intelligence #Cheating #Computer science #Data science #Economics #Epistemology #Experimental Behavioral Economics Studies #Incentive #Law #Microeconomics #Normative #Political science #Popularity #Principal (computer security) #Process (computing) #Psychology #Quality (philosophy) #Replication (statistics) #Selection (genetic algorithm) #Social psychology #Species Distribution and Climate Change #acm:62A01 #acm:62Pxx #acm:91D10 #msc:62A01 #msc:62Pxx #msc:91D10 #physics.soc-ph #scientometrics and bibliometrics research #stat.AP

paper · pdf · doi:10.1098/rsos.160384

published in Royal Society Open Science 3(9), 160384 (Royal Society) · 41 pages, 7 figures

arxiv created 2016/05/31 · arxiv published 2016/05/31 · openalex publication_date 2016/09/01 · arxiv updated 2016/10/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05

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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