Movatterモバイル変換

[0]ホーム
URL:

Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
Thehttps:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

NIH NLM Logo
Log inShow account info
Access keysNCBI HomepageMyNCBI HomepageMain ContentMain Navigation
pubmed logo
Advanced Clipboard
User Guide

Full text links

Free PMC article
Full text links

Actions

Share

.2019 Jan;20(4):604-607.
doi: 10.22074/cellj.2019.5992. Epub 2018 Aug 1.

Why, When and How to Adjust Your P Values?

Affiliations

Why, When and How to Adjust Your P Values?

Mohieddin Jafari et al. Cell J.2019 Jan.

Abstract

Currently, numerous papers are published reporting analysis of biological data at different omics levels by making statistical inferences. Of note, many studies, as those published in this Journal, report association of gene(s) at the genomic and transcriptomic levels by undertaking appropriate statistical tests. For instance, genotype, allele or haplotype frequencies at the genomic level or normalized expression levels at the transcriptomic level are compared between the case and control groups using the Chi-square/Fisher's exact test or independent (i.e. two-sampled) t-test respectively, with this culminating into a single numeric, namely the P value (or the degree of the false positive rate), which is used to make or break the outcome of the association test. This approach has flaws but nevertheless remains a standard and convenient approach in association studies. However, what becomes a critical issue is that the same cut-off is used when 'multiple' tests are undertaken on the same case-control (or any pairwise) comparison. Here, in brevity, we present what the P value represents, and why and when it should be adjusted. We also show, with worked examples, how to adjust P values for multiple testing in the R environment for statistical computing (http://www.R-project.org).

Keywords: Bias; Gene Expression Profiling; Genetic Variation; Research Design; Statistical Data Analyses.

Copyright© by Royan Institute. All rights reserved.

PubMed Disclaimer

Conflict of interest statement

There is no conflict of interest in this study.

Figures

Fig.1
Fig.1
Comparison of the two multiple testing adjustment methods in a matrix plot. The distribution of 500 random P values before and after adjustmentis represented on the diagonal. The upper and lower triangles show the pairwise correlation coefficients and scatter plot between raw and adjusted Pvalues respectively.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Fisher RA. Tests of significance in harmonic analysis. Proc R Soc A Math Phys Eng Sci. 1929;125(796):594–599.
    1. Balding DJ. A tutorial on statistical methods for population association studies. Nat Rev Genet. 2006;7(10):781–791. - PubMed
    1. Cohen J. The earth is round (p<.05): rejoinder. Am Psychol. 1995;50(12):1103–1103.
    1. Sterne JA, Davey Smith G. Sifting the evidence-what’s wrong with significance tests? BMJ. 2001;322(7280):226–231. - PMC - PubMed
    1. Lee JK. Statistical bioinformatics. 1st ed. New Jersey: John Wiley & Sons Inc; 2010.

LinkOut - more resources

Full text links
Free PMC article
Cite
Send To

NCBI Literature Resources

MeSHPMCBookshelfDisclaimer

The PubMed wordmark and PubMed logo are registered trademarks of the U.S. Department of Health and Human Services (HHS). Unauthorized use of these marks is strictly prohibited.

[8]ページ先頭
©2009-2025 Movatter.jp