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Empirical assessment of published effect sizes and power in the recent cognitive neuroscience and psychology literature

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  • Denes Szucs
  • John P A Ioannidis

Abstract

We have empirically assessed the distribution of published effect sizes and estimated power by analyzing 26,841 statistical records from 3,801 cognitive neuroscience and psychology papers published recently. The reported median effect size was D = 0.93 (interquartile range: 0.64–1.46) for nominally statistically significant results and D = 0.24 (0.11–0.42) for nonsignificant results. Median power to detect small, medium, and large effects was 0.12, 0.44, and 0.73, reflecting no improvement through the past half-century. This is so because sample sizes have remained small. Assuming similar true effect sizes in both disciplines, power was lower in cognitive neuroscience than in psychology. Journal impact factors negatively correlated with power. Assuming a realistic range of prior probabilities for null hypotheses, false report probability is likely to exceed 50% for the whole literature. In light of our findings, the recently reported low replication success in psychology is realistic, and worse performance may be expected for cognitive neuroscience.Author summary: Biomedical science, psychology, and many other fields may be suffering from a serious replication crisis. In order to gain insight into some factors behind this crisis, we have analyzed statistical information extracted from thousands of cognitive neuroscience and psychology research papers. We established that the statistical power to discover existing relationships has not improved during the past half century. A consequence of low statistical power is that research studies are likely to report many false positive findings. Using our large dataset, we estimated the probability that a statistically significant finding is false (called false report probability). With some reasonable assumptions about how often researchers come up with correct hypotheses, we conclude that more than 50% of published findings deemed to be statistically significant are likely to be false. We also observed that cognitive neuroscience studies had higher false report probability than psychology studies, due to smaller sample sizes in cognitive neuroscience. In addition, the higher the impact factors of the journals in which the studies were published, the lower was the statistical power. In light of our findings, the recently reported low replication success in psychology is realistic, and worse performance may be expected for cognitive neuroscience.

Suggested Citation

  • Denes Szucs & John P A Ioannidis, 2017. "Empirical assessment of published effect sizes and power in the recent cognitive neuroscience and psychology literature," PLOS Biology, Public Library of Science, vol. 15(3), pages 1-18, March.
  • Handle: RePEc:plo:pbio00:2000797
    DOI: 10.1371/journal.pbio.2000797
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    References listed on IDEAS

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    1. John P A Ioannidis, 2005. "Why Most Published Research Findings Are False," PLOS Medicine, Public Library of Science, vol. 2(8), pages 1-1, August.
    2. Coosje L S Veldkamp & Michèle B Nuijten & Linda Dominguez-Alvarez & Marcel A L M van Assen & Jelte M Wicherts, 2014. "Statistical Reporting Errors and Collaboration on Statistical Analyses in Psychological Science," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-19, December.
    3. John P A Ioannidis, 2014. "How to Make More Published Research True," PLOS Medicine, Public Library of Science, vol. 11(10), pages 1-6, October.
    4. Sellke T. & Bayarri M. J. & Berger J. O., 2001. "Calibration of rho Values for Testing Precise Null Hypotheses," The American Statistician, American Statistical Association, vol. 55, pages 62-71, February.
    5. Michael D. Jennions & Anders Pape Møller, 2003. "A survey of the statistical power of research in behavioral ecology and animal behavior," Behavioral Ecology, International Society for Behavioral Ecology, vol. 14(3), pages 438-445, May.
    6. David A. Harrison & Anthony R. Brady, 2004. "Sample size and power calculations using the noncentral t-distribution," Stata Journal, StataCorp LP, vol. 4(2), pages 142-153, June.
    7. Jesse Chandler & et. al, 2016. "Response to Comment on "Estimating the Reproducibility of Psychological Science"," Mathematica Policy Research Reports cff9c2f16bb544c4bcca530c0, Mathematica Policy Research.
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    2. Kathryn N. Vasilaky & J. Michelle Brock, 2020. "Power(ful) guidelines for experimental economists," Journal of the Economic Science Association, Springer;Economic Science Association, vol. 6(2), pages 189-212, December.
    3. Augusteijn, Hilde Elisabeth Maria & Wicherts, Jelte M. & Sijtsma, Klaas & van Assen, Marcel A. L. M., 2023. "Quality assessment of scientific manuscripts in peer review and education," OSF Preprints 7dc6a, Center for Open Science.
    4. Livingston, Jeffrey A. & Rasulmukhamedov, Rustam, 2023. "On the Interpretation of Giving in Dictator Games When the Recipient is a Charity," Journal of Economic Behavior & Organization, Elsevier, vol. 208(C), pages 275-285.
    5. Esteban Morales & Erin C McKiernan & Meredith T Niles & Lesley Schimanski & Juan Pablo Alperin, 2021. "How faculty define quality, prestige, and impact of academic journals," PLOS ONE, Public Library of Science, vol. 16(10), pages 1-13, October.
    6. Kathryn Vasilaky & Sofía Martínez Sáenz & Radost Stanimirova & Daniel Osgood, 2020. "Perceptions of Farm Size Heterogeneity and Demand for Group Index Insurance," Games, MDPI, vol. 11(1), pages 1-21, March.
    7. Filip Melinscak & Dominik R Bach, 2020. "Computational optimization of associative learning experiments," PLOS Computational Biology, Public Library of Science, vol. 16(1), pages 1-23, January.

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