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A Poor Prognosis for the Diagnostic Screening Critique of Statistical Tests

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  • Mayo, Deborah
  • Morey, Richard Donald

Abstract

Recently, a number of statistical reformers have argued for conceptualizing significance testing as analogous to diagnostic testing, with a "base rate" of true nulls and a test's error probabilities used to compute a "positive predictive value" or "false discovery rate". These quantities are used to critique statistical and scientific practice. We argue against this; these quantities are not relevant for evaluating statistical tests, they add to the confusion over significance testing, and they take the focus away from what matters: evidence.

Suggested Citation

  • Mayo, Deborah & Morey, Richard Donald, 2017. "A Poor Prognosis for the Diagnostic Screening Critique of Statistical Tests," OSF Preprints ps38b, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:ps38b
    DOI: 10.31219/osf.io/ps38b
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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. 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.
    3. 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.
    4. Steven Goodman & Sander Greenland, 2007. "Why Most Published Research Findings Are False: Problems in the Analysis," PLOS Medicine, Public Library of Science, vol. 4(4), pages 1-1, April.
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    Cited by:

    1. Sam Sims & Jake Anders & Matthew Inglis & Hugues Lortie-Forgues, 2020. "Quantifying 'promising trials bias' in randomized controlled trials in education," CEPEO Working Paper Series 20-16, UCL Centre for Education Policy and Equalising Opportunities, revised Nov 2020.

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