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Measurement of Evidence and Evidence of Measurement

Author

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  • Vieland Veronica J

    (The Research Institute at Nationwide Children’s Hospital and The Ohio State University)

  • Hodge Susan E

    (New York State Psychiatric Institute and Columbia University)

Abstract

One important use of statistical methods in application to biological data is measurement of evidence, or assessment of the degree to which data support one or another hypothesis. While there is a small literature on this topic, it seems safe to say that consensus has not yet been reached regarding how best, or most accurately, to measure statistical evidence. Here, we propose considering the problem as a measurement problem, rather than as a statistical problem per se, and we explore the consequences of this shift in perspective. Our arguments here are part of an ongoing research program focused on exploiting deep parallelisms between foundations of thermodynamics and foundations of “evidentialism,” in order to derive an absolute scale for the measurement of evidence, a general framework in the context of which that scale is validated, and the many ancillary benefits that come from having such a framework in place.

Suggested Citation

  • Vieland Veronica J & Hodge Susan E, 2011. "Measurement of Evidence and Evidence of Measurement," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-11, July.
    • Handle: RePEc:bpj:sagmbi:v:10:y:2011:i:1:n:35
    DOI: 10.2202/1544-6115.1682
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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.
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    Cited by:

    1. Kimberly A Walters & Yungui Huang & Marco Azaro & Kathleen Tobin & Thomas Lehner & Linda M Brzustowicz & Veronica J Vieland, 2014. "Meta-Analysis of Repository Data: Impact of Data Regularization on NIMH Schizophrenia Linkage Results," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-8, January.
    2. Veronica J Vieland & Sang-Cheol Seok, 2021. "The PPLD has advantages over conventional regression methods in application to moderately sized genome-wide association studies," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-22, September.

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