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The p -Value You Can’t Buy

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  • Eugene Demidenko

Abstract

There is growing frustration with the concept of the p -value. Besides having an ambiguous interpretation, the p- value can be made as small as desired by increasing the sample size, n . The p -value is outdated and does not make sense with big data: Everything becomes statistically significant. The root of the problem with the p- value is in the mean comparison. We argue that statistical uncertainty should be measured on the individual, not the group, level. Consequently, standard deviation (SD), not standard error (SE), error bars should be used to graphically present the data on two groups. We introduce a new measure based on the discrimination of individuals/objects from two groups, and call it the D -value. The D -value can be viewed as the n -of-1 p -value because it is computed in the same way as p while letting n equal 1. We show how the D -value is related to discrimination probability and the area above the receiver operating characteristic (ROC) curve. The D -value has a clear interpretation as the proportion of patients who get worse after the treatment, and as such facilitates to weigh up the likelihood of events under different scenarios.[Received January 2015. Revised June 2015.]

Suggested Citation

  • Eugene Demidenko, 2016. "The p -Value You Can’t Buy," The American Statistician, Taylor & Francis Journals, vol. 70(1), pages 33-38, February.
    • Handle: RePEc:taf:amstat:v:70:y:2016:i:1:p:33-38
    DOI: 10.1080/00031305.2015.1069760
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    References listed on IDEAS

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    1. Regina Nuzzo, 2014. "Scientific method: Statistical errors," Nature, Nature, vol. 506(7487), pages 150-152, February.
    2. Eugene Demidenko, 2012. "Confidence intervals and bands for the binormal ROC curve revisited," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(1), pages 67-79, March.
    3. Gelman, Andrew & Stern, Hal, 2006. "The Difference Between," The American Statistician, American Statistical Association, vol. 60, pages 328-331, November.
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