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Activity Number: 116
Type: Topic Contributed
Date/Time: Monday, August 1, 2016 : 8:30 AM to 10:20 AM
Sponsor: ENAR
Abstract #318947 View Presentation
Title: P, Sorry, D-Value
Author(s): Eugene Demidenko*
Companies: Dartmouth College
Keywords: statistical significance ; ROC curve ; effect size ; nonparametric statistics ; practical significance ; hypothesis testing

Statistical hypothesis testing and the associated P-value are at the heart of empirical evidence sciences and yet several prominent statisticians recently warn the widespread abuse of the P-value and its contribution to false positive findings. The current discussion follows up a paper of the same author "The P-value you can't buy" and suggests an alternative, called the D-value, which has a more clear interpretation. Unlike the P-value, the new measure does not decrease to zero when the sample size, n, goes to infinity. The D-value is computed by the same formula as the P-value but uses n=1, and therefore may be viewed as the n-of-1 P-value. The D-value is at the crossroads of major statistical concepts such as the area under ROC curve, Mann-Whitney U test, and effect size. It has a clear interpretation as the probability that a randomly chosen patient from the treatment group gets worse than a randomly chosen patient from the placebo group. Thus, unlike P-value with the emphasis on the average, D-value reflects the individual comparison. The D-value is in unison with the voices from medical doctors: "We treat not a group of patients but an individual."

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