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The Preregistration Revolution

Author

Listed:
  • Nosek, Brian A.

    (University of Virginia)

  • Ebersole, Charles R.

    (University of Virginia)

  • DeHaven, Alexander Carl

    (Center for Open Science)

  • Mellor, David Thomas

    (Center for Open Science)

Abstract

Progress in science relies on generating hypotheses with existing observations and testing hypotheses with new observations. This distinction between postdiction and prediction is appreciated conceptually, but is not respected in practice. Mistaking generation of postdictions with testing of predictions reduces the credibility of research findings. However, ordinary biases in human reasoning such as hindsight bias make it hard to avoid this mistake. An effective solution is to define the research questions and analysis plan prior to observing the research outcomes--a process called preregistration. A variety of practical strategies are available to make the best possible use of preregistration in circumstances that fall short of the ideal application, such as when the data are pre-existing. Services are now available for preregistration across all disciplines facilitating a rapid increase in the practice. Widespread adoption of preregistration will increase distinctiveness between hypothesis generation and hypothesis testing and will improve the credibility of research findings.

Suggested Citation

  • Nosek, Brian A. & Ebersole, Charles R. & DeHaven, Alexander Carl & Mellor, David Thomas, 2018. "The Preregistration Revolution," OSF Preprints 2dxu5, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:2dxu5
    DOI: 10.31219/osf.io/2dxu5
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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. Marcel Fafchamps & Julien Labonne, 2016. "Using Split Samples to Improve Inference about Causal Effects," NBER Working Papers 21842, National Bureau of Economic Research, Inc.
    3. 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.
    4. Andrew Cockburn, 2017. "Long-term data as infrastructure: a comment on Ihle et al," Behavioral Ecology, International Society for Behavioral Ecology, vol. 28(2), pages 357-357.
    5. Ronald L. Wasserstein & Nicole A. Lazar, 2016. "The ASA's Statement on p -Values: Context, Process, and Purpose," The American Statistician, Taylor & Francis Journals, vol. 70(2), pages 129-133, May.
    6. Robert MacCoun & Saul Perlmutter, 2015. "Blind analysis: Hide results to seek the truth," Nature, Nature, vol. 526(7572), pages 187-189, October.
    7. Fafchamps, Marcel & Labonne, Julien, 2017. "Using Split Samples to Improve Inference on Causal Effects," Political Analysis, Cambridge University Press, vol. 25(4), pages 465-482, October.
    8. Christensen-Szalanski, Jay J. J. & Willham, Cynthia Fobian, 1991. "The hindsight bias: A meta-analysis," Organizational Behavior and Human Decision Processes, Elsevier, vol. 48(1), pages 147-168, February.
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