An Introduction to Second-Generation p-Values
By Jeffrey D. Blume, Robert A. Greevy Jr., V. Welty, Jeffrey R. Smith, W. Dupont. American Statistician, 2019
88 citations recorded by the source corpus.
ABSTRACT Second generation p-values preserve the simplicity that has made p-values popular while resolving critical flaws that promote misinterpretation of data, distraction by trivial effects, and unreproducible assessments of data. The second-generation p-value (SGPV) is an extension that formally accounts for scientific relevance by using a composite null hypothesis that captures null and scientifically trivial effects. Because the majority of spurious findings are small effects that are technically nonnull but practically indistinguishable from the null, the second-generation approach greatly reduces the likelihood of a false discovery. SGPVs promote transparency, rigor and reproducibility of scientific results by a priori identifying which candidate hypotheses are practically meaningful and by providing a more reliable statistical summary of when the data are compatible with the candidate hypotheses or null hypotheses, or when the data are inconclusive. We illustrate the importance of these advances using a dataset of 247,000 single-nucleotide polymorphisms, i.e., genetic markers that are potentially associated with prostate cancer.
Subfields: Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/649be8b7020e434de7d32fe69aa7e59fe47f113c
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Citations in the corpus
- Statistical significance: p value, 0.05 threshold, and applications to radiomics—reasons for a conservative approach (2020, background): “An interesting advancement is the so-called secondgeneration p value, based on an expanded null hypothesis [40].”
- Moving to a World Beyond “p < 0.05” (2019, unknown): “Acknowledging this, Brownstein et al. (2019) point out that expert judgment and knowledge are required in all stages of the scientific method.”