The harmonic mean p -value for combining dependent tests
Daniel J. Wilson · 2019 · Proceedings of the National Academy of Sciences
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
-value (HMP), which controls the FWER while greatly improving statistical power by combining dependent tests using generalized central limit theorem. I show that the HMP effortlessly combines information to detect statistically significant signals among groups of individually nonsignificant hypotheses in examples of a human GWAS for neuroticism and a joint human-pathogen GWAS for hepatitis C viral load. The HMP simultaneously tests all ways to group hypotheses, allowing the smallest groups of hypotheses that retain significance to be sought. The power of the HMP to detect significant hypothesi
Abstract by Daniel J. Wilson, Proceedings of the National Academy of Sciences (2019) — licensed CC BY 4.0.
About to run something similar?
Run an AI Precheck on your own design to catch failure modes like this one before you spend the time. Your first desk check is free.
Related failures
Channeling Fisher: Randomization Tests and the Statistical Insignificance of Seemingly Significant Experimental Results*
Negative / Null Result ReportGeneralizability of heterogeneous treatment effect estimates across samples
Negative / Null Result ReportNumerical predictors of arithmetic success in grades 1–6
Negative / Null Result ReportMethods Matter: p-Hacking and Publication Bias in Causal Analysis in Economics
Negative / Null Result ReportShould multiple imputation be the method of choice for handling missing data in randomized trials?
Negative / Null Result ReportMisuse of DeLong test to compare AUCs for nested models
WASTE indexes this work — it does not host or republish it. Failure-type classification is automated and approximate.
Metadata source: OpenAlex · DOI 10.1073/pnas.1814092116