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Finding the right power balance: Better study design and collaboration can reduce dependence on statistical power

Author

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  • Shinichi Nakagawa
  • Malgorzata Lagisz
  • Yefeng Yang
  • Szymon M Drobniak

Abstract

Power analysis currently dominates sample size determination for experiments, particularly in grant and ethics applications. Yet, this focus could paradoxically result in suboptimal study design because publication biases towards studies with the largest effects can lead to the overestimation of effect sizes. In this Essay, we propose a paradigm shift towards better study designs that focus less on statistical power. We also advocate for (pre)registration and obligatory reporting of all results (regardless of statistical significance), better facilitation of team science and multi-institutional collaboration that incorporates heterogenization, and the use of prospective and living meta-analyses to generate generalizable results. Such changes could make science more effective and, potentially, more equitable, helping to cultivate better collaborations.Statistical power analysis currently dominates experimental design. In this Essay, the authors argue that we should move away from the current focus on power analysis and instead encourage smaller scale studies and collaborative projects.

Suggested Citation

  • Shinichi Nakagawa & Malgorzata Lagisz & Yefeng Yang & Szymon M Drobniak, 2024. "Finding the right power balance: Better study design and collaboration can reduce dependence on statistical power," PLOS Biology, Public Library of Science, vol. 22(1), pages 1-17, January.
  • Handle: RePEc:plo:pbio00:3002423
    DOI: 10.1371/journal.pbio.3002423
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    References listed on IDEAS

    as
    1. Shinichi Nakagawa & Edward R. Ivimey-Cook & Matthew J. Grainger & Rose E. O’Dea & Samantha Burke & Szymon M. Drobniak & Elliot Gould & Erin L. Macartney & April Robin Martinig & Kyle Morrison & Matthi, 2023. "Method Reporting with Initials for Transparency (MeRIT) promotes more granularity and accountability for author contributions," Nature Communications, Nature, vol. 14(1), pages 1-5, December.
    2. repec:plo:pbio00:2005413 is not listed on IDEAS
    3. Christopher Allen & David M A Mehler, 2019. "Open science challenges, benefits and tips in early career and beyond," PLOS Biology, Public Library of Science, vol. 17(5), pages 1-14, May.
    4. Valentin Amrhein & David Trafimow & Sander Greenland, 2019. "Inferential Statistics as Descriptive Statistics: There Is No Replication Crisis if We Don’t Expect Replication," The American Statistician, Taylor & Francis Journals, vol. 73(S1), pages 262-270, March.
    5. repec:plo:pbio00:3000587 is not listed on IDEAS
    6. John P. A. Ioannidis & T. D. Stanley & Hristos Doucouliagos, 2017. "The Power of Bias in Economics Research," Economic Journal, Royal Economic Society, vol. 127(605), pages 236-265, October.
    7. Ronald L. Wasserstein & Allen L. Schirm & Nicole A. Lazar, 2019. "Moving to a World Beyond “p," The American Statistician, Taylor & Francis Journals, vol. 73(S1), pages 1-19, March.
    8. Langdalen, Henrik & Abrahamsen, Eirik Bjorheim & Selvik, Jon Tømmerås, 2020. "On the importance of systems thinking when using the ALARP principle for risk management," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    9. Nicholas A. Coles & Lisa M. DeBruine & Flavio Azevedo & Heidi A. Baumgartner & Michael C. Frank, 2023. "‘Big team’ science challenges us to reconsider authorship," Nature Human Behaviour, Nature, vol. 7(5), pages 665-667, May.
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