Youden’s J and the Bi Error Method

Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 284)


Incorrect usage of p-values, particularly within the context of significance testing using the arbitrary .05 threshold, has become a major problem in modern statistical practice. The prevalence of this problem can be traced back to the context-free 5-step method commonly taught to undergraduates: we teach it because it is what is done, and we do it because it is what we are taught. This hold particularly true for practitioners of statistics who are not formal statisticians. Thus, in order to improve scientific practice and overcome statistical dichotomania, an accessible replacement for the 5-step method is warranted. We propose a method foundational on the utilization of the Youden Index as a potential decision threshold, which has been shown in the literature to be effective in conjunction with neutral zones. Unlike the traditional 5-step method, our 5-step method (the Bi Error method) allows for neutral results, does not require p-values, and does not provide any default threshold values. Instead, our method explicitly requires contextual error analysis as well as quantification of statistical power. Furthermore, and in part due to its lack of usage of p-values, the method sports improved accessibility. This accessibility is supported by a generalized analytical derivation of the Youden Index.


Youden Index p-Value Type II error 



We wish to thank Dr. Lynne Stokes and the two reviewers for their feedback, which substantially improved the paper. We additionally thank Dr. Dan Jeske for introducing us to the literature on hypothesis testing with neutral zones, as well as Michael Watts and Austin Marstaller for proofreading the manuscript.


  1. 1.
    Beigel, J., et al.: Remdesivir for the treatment of COVID-19. New England J. Med. 383, 1813–1826 (2020)CrossRefGoogle Scholar
  2. 2.
    Cassidy, S.A., Dimova, R., Giguère, B., Spence, J.R., Stanley, D.J.: Failing grade: 89% of introduction-to- psychology textbooks that define or explain statistical significance do so incorrectly. Adv. Methods Pract. Psychol. Sci. 2(3), 233–239 (2019)CrossRefGoogle Scholar
  3. 3.
    Fluss, R., Faraggi, D., Reiser, B.: Estimation of the Youden index and it-s associated cutoff point. Biom. J. 47(4), 129–133 (2005)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Harrell, F.: Statistical errors in the medical literature.
  5. 5.
    Ioanndis, J.: What have we (not) learnt from millions of scientific papers with \(p-\)values? Am. Stat. 73(Sup.1), 20–25 (2019)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Jeske, D.: Special collection on \(p-\)values. Am. Stat. 73(Sup.1), 1–401 (2019)Google Scholar
  7. 7.
    Jeske, D.: Alternatives to the traditional \(p-\)value. National Institute of Statistical Science, Webinar (2019)Google Scholar
  8. 8.
    Jeske, D.: Digging deeper into \(p\)-values: Webinar follow-up with three authors. National Institute of Statistical Science, Webinar (2019)Google Scholar
  9. 9.
    Jeske, D., Smith, S.: Maximizing the usefulness of statistical classifiers for two populations with illustrative applications. Stat. Methods Med. Res. 27, 1–15 (2016)Google Scholar
  10. 10.
    Li, C.: Partial Youden index and cut point selection. J. Biopharmec. Stat. 20(5), 1520–5711 (2018)Google Scholar
  11. 11.
    Liu, X.: Classification accuracy and cut point selection. Stat. Med. 31, 2676–2686 (2011)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Norrie, J.: Remdesivir for COVID-19: challenges of Underpowered studies. Lancet 395, 1569 (2020)Google Scholar
  13. 13.
    R Core Team: R: A language and environment for statistical computing. R Foundation for Statistical Computing (2013)Google Scholar
  14. 14.
    Senn, S.: Dichotomania: an obsessive-compulsive disorder that is badly affecting the quality of analysis in pharmaceutical trials. In: Proceedings of the International Statistical Institute, ISI Sydney, pp. 1–15 (2005)Google Scholar
  15. 15.
    Schistermann, E., Perkins, N.: Partial Youden index and cut point selection. Commun. Stat. 36(3), 549–563 (2007)CrossRefGoogle Scholar
  16. 16.
    Wasserstein, R., Lazar, N.: The ASA statement on \(p-\)values: context, process, and purpose. Am. Stat. 70(2), 129–133 (2016)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Wasserstein, R., Lazar, N.: Moving towards a world beyond \(p<.05\). Am. Stat. 73(Sup.1), 1–19 (2019)CrossRefGoogle Scholar
  18. 18.
    Dahl, D.B., Scott, D. Roosen, C., Magnusson, A., Swinton, J.: xtable: export tables to LaTeX or HTML, R Foundation for Statistical Computing, R package version 1.8-4 (2019)Google Scholar
  19. 19.
    Youden, W.J.: Index for rating diagnostic tests. Cancer 3(1), 32–35 (1950)CrossRefGoogle Scholar

Copyright information

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021

Authors and Affiliations

  1. 1.Statistical ScienceSouthern Methodist UniversityDallasUSA

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