IDEAS home Printed from https://ideas.repec.org/p/osf/osfxxx/gmfs9.html

Why is the statistical revolution not progressing? Vicious cycle of the scientific reform

Author

Listed:
  • Bialek, Michal

    (University of Waterloo)

  • Misiak, Michał

    (University of Wroclaw)

  • Dziekan, Martyna

Abstract

To analyze the results of the research, behavioral scientists widely use a statistical rule that sets the significance level to 0.05. Recently, two recommendations on how to improve statistical inference were published: to redefine statistical significance to 0.005, and to select and justify the alpha. We analyzed the empirical work that cited the original recommendation papers, as well as the papers published by the scientist that co-authored the publications. About half of the numerous papers citing these recommendations adhered to them already in the first year since their publication. What is striking, the original authors that proposed the recommendations followed their own recommendations only in 6% of their empirical work. We surveyed the authors asking them to identify major obstacles they experienced while trying to implement their own recommendations, and obstacles they think others could expect or experience.

Suggested Citation

  • Bialek, Michal & Misiak, Michał & Dziekan, Martyna, 2021. "Why is the statistical revolution not progressing? Vicious cycle of the scientific reform," OSF Preprints gmfs9, Center for Open Science.
  • Handle: RePEc:osf:osfxxx:gmfs9
    DOI: 10.31219/osf.io/gmfs9
    as

    Download full text from publisher

    File URL: https://osf.io/download/614c71d615e9a4004b923c2f/
    Download Restriction: no

    File URL: https://libkey.io/10.31219/osf.io/gmfs9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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. Georgios Gerasimou, 2016. "Asymmetric dominance, deferral, and status quo bias in a behavioral model of choice," Theory and Decision, Springer, vol. 80(2), pages 295-312, February.
    3. Daniel J. Benjamin & James O. Berger & Magnus Johannesson & Brian A. Nosek & E.-J. Wagenmakers & Richard Berk & Kenneth A. Bollen & Björn Brembs & Lawrence Brown & Colin Camerer & David Cesarini & Chr, 2018. "Redefine statistical significance," Nature Human Behaviour, Nature, vol. 2(1), pages 6-10, January.
      • Daniel Benjamin & James Berger & Magnus Johannesson & Brian Nosek & E. Wagenmakers & Richard Berk & Kenneth Bollen & Bjorn Brembs & Lawrence Brown & Colin Camerer & David Cesarini & Christopher Chambe, 2017. "Redefine Statistical Significance," Artefactual Field Experiments 00612, The Field Experiments Website.
    4. Abel Brodeur & Nikolai Cook & Anthony Heyes, 2020. "Methods Matter: p-Hacking and Publication Bias in Causal Analysis in Economics," American Economic Review, American Economic Association, vol. 110(11), pages 3634-3660, November.
    5. Megan L Head & Luke Holman & Rob Lanfear & Andrew T Kahn & Michael D Jennions, 2015. "The Extent and Consequences of P-Hacking in Science," PLOS Biology, Public Library of Science, vol. 13(3), pages 1-15, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. repec:osf:osfxxx:gmfs9_v1 is not listed on IDEAS
    2. Guillaume Coqueret, 2023. "Forking paths in financial economics," Papers 2401.08606, arXiv.org.
    3. Gonzalez-Jimenez, David & Capozza, Francesco & Dirkmaat, Thomas & van de Veer, Evelien & van Druten, Amber & Baillon, Aurélien, 2025. "Falling and failing (to learn): Evidence from a nation-wide cybersecurity field experiment with SMEs," Journal of Economic Behavior & Organization, Elsevier, vol. 230(C).
    4. Dudek, Thomas & Brenøe, Anne Ardila & Feld, Jan & Rohrer, Julia, 2022. "No Evidence That Siblings' Gender Affects Personality across Nine Countries," IZA Discussion Papers 15137, IZA Network @ LISER.
    5. Jasper Brinkerink, 2023. "When Shooting for the Stars Becomes Aiming for Asterisks: P-Hacking in Family Business Research," Entrepreneurship Theory and Practice, , vol. 47(2), pages 304-343, March.
    6. repec:osf:metaar:s4b65_v1 is not listed on IDEAS
    7. Rinne, Sonja, 2024. "Estimating the merit-order effect using coarsened exact matching: Reconciling theory with the empirical results to improve policy implications," Energy Policy, Elsevier, vol. 185(C).
    8. Stephan B Bruns & John P A Ioannidis, 2016. "p-Curve and p-Hacking in Observational Research," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-13, February.
    9. Colin F. Camerer & Anna Dreber & Felix Holzmeister & Teck-Hua Ho & Jürgen Huber & Magnus Johannesson & Michael Kirchler & Gideon Nave & Brian A. Nosek & Thomas Pfeiffer & Adam Altmejd & Nick Buttrick , 2018. "Evaluating the replicability of social science experiments in Nature and Science between 2010 and 2015," Nature Human Behaviour, Nature, vol. 2(9), pages 637-644, September.
    10. Brodeur, Abel & Cook, Nikolai & Heyes, Anthony, 2022. "We Need to Talk about Mechanical Turk: What 22,989 Hypothesis Tests Tell us about p-Hacking and Publication Bias in Online Experiments," GLO Discussion Paper Series 1157, Global Labor Organization (GLO).
    11. Oliver Braganza, 2020. "A simple model suggesting economically rational sample-size choice drives irreproducibility," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-19, March.
    12. Christophe Pérignon & Olivier Akmansoy & Christophe Hurlin & Anna Dreber & Felix Holzmeister & Juergen Huber & Magnus Johanneson & Michael Kirchler & Albert J Menkveld & Michael Razen & Utz Weitzel, 2022. "Reproducibility of Empirical Results: Evidence from 1,000 Tests in Finance," Working Papers hal-03810013, HAL.
    13. Williams, Cole Randall, 2019. "How redefining statistical significance can worsen the replication crisis," Economics Letters, Elsevier, vol. 181(C), pages 65-69.
    14. Freuli, Francesca & Held, Leonhard & Heyard, Rachel, 2022. "Replication Success under Questionable Research Practices - A Simulation Study," I4R Discussion Paper Series 2, The Institute for Replication (I4R).
    15. Felix Chopra & Ingar Haaland & Christopher Roth & Andreas Stegmann, 2024. "The Null Result Penalty," The Economic Journal, Royal Economic Society, vol. 134(657), pages 193-219.
    16. Graham Elliott & Nikolay Kudrin & Kaspar Wuthrich, 2022. "The Power of Tests for Detecting $p$-Hacking," Papers 2205.07950, arXiv.org, revised Aug 2025.
    17. Brodeur, Abel & Cook, Nikolai M. & Heyes, Anthony & Wright, Taylor, 2025. "Media Stars: Statistical Significance and Research Impact," I4R Discussion Paper Series 254, The Institute for Replication (I4R).
    18. Martin E Héroux & Janet L Taylor & Simon C Gandevia, 2015. "The Use and Abuse of Transcranial Magnetic Stimulation to Modulate Corticospinal Excitability in Humans," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-10, December.
    19. Pierre J C Chuard & Milan Vrtílek & Megan L Head & Michael D Jennions, 2019. "Evidence that nonsignificant results are sometimes preferred: Reverse P-hacking or selective reporting?," PLOS Biology, Public Library of Science, vol. 17(1), pages 1-7, January.
    20. Bernhard Voelkl & Lucile Vogt & Emily S Sena & Hanno Würbel, 2018. "Reproducibility of preclinical animal research improves with heterogeneity of study samples," PLOS Biology, Public Library of Science, vol. 16(2), pages 1-13, February.
    21. Valérie Orozco & Christophe Bontemps & Élise Maigné & Virginie Piguet & Annie Hofstetter & Anne Marie Lacroix & Fabrice Levert & Jean-Marc Rousselle, 2017. "How to make a pie? Reproducible Research for Empirical Economics & Econometrics," Post-Print hal-01939942, HAL.
    22. Brodeur, Abel & Cook, Nikolai & Heyes, Anthony, 2022. "We Need to Talk about Mechanical Turk: What 22,989 Hypothesis Tests Tell Us about Publication Bias and p-Hacking in Online Experiments," IZA Discussion Papers 15478, IZA Network @ LISER.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:osf:osfxxx:gmfs9. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: OSF (email available below). General contact details of provider: https://osf.io/preprints/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.