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A sample size calibration approach for the p-value problem in huge samples

Authors
Park, YousungJeon, SaebomKwon, Tae Yeon
Issue Date
9월-2018
Publisher
KOREAN STATISTICAL SOC
Keywords
huge sample; p-value problem; subject-matter significance; Monte Carlo; sample size calibration
Citation
COMMUNICATIONS FOR STATISTICAL APPLICATIONS AND METHODS, v.25, no.5, pp.545 - 557
Indexed
SCOPUS
KCI
Journal Title
COMMUNICATIONS FOR STATISTICAL APPLICATIONS AND METHODS
Volume
25
Number
5
Start Page
545
End Page
557
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/73228
DOI
10.29220/CSAM.2018.25.5.545
ISSN
2287-7843
Abstract
The inclusion of covariates in the model often affects not only the estimates of meaningful variables of interest but also its statistical significance. Such gap between statistical and subject-matter significance is a critical issue in huge sample studies. A popular huge sample study, the sample cohort data from Korean National Health Insurance Service, showed such gap of significance in the inference for the effect of obesity on cause of mortality, requiring careful consideration. In this regard, this paper proposes a sample size calibration method based on a Monte Carlo t (or z)-test approach without Monte Carlo simulation, and also proposes a test procedure for subject-matter significance using this calibration method in order to complement the deflated p-value in the huge sample size. Our calibration method shows no subject-matter significance of the obesity paradox regardless of race, sex, and age groups, unlike traditional statistical suggestions based on p-values.
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