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An open-source software program for performing Bonferroni and related corrections for multiple comparisons

Increased type I error resulting from multiple statistical comparisons remains a common problem in the scientific literature. This may result in the reporting and promulgation of spurious findings. One approach to this problem is to correct groups of P-values for “family-wide significance” using a B...

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Detalles Bibliográficos
Autores principales: Lesack, Kyle, Naugler, Christopher
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Medknow Publications & Media Pvt Ltd 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3263024/
https://www.ncbi.nlm.nih.gov/pubmed/22276243
http://dx.doi.org/10.4103/2153-3539.91130
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author Lesack, Kyle
Naugler, Christopher
author_facet Lesack, Kyle
Naugler, Christopher
author_sort Lesack, Kyle
collection PubMed
description Increased type I error resulting from multiple statistical comparisons remains a common problem in the scientific literature. This may result in the reporting and promulgation of spurious findings. One approach to this problem is to correct groups of P-values for “family-wide significance” using a Bonferroni correction or the less conservative Bonferroni-Holm correction or to correct for the “false discovery rate” with a Benjamini-Hochberg correction. Although several solutions are available for performing this correction through commercially available software there are no widely available easy to use open source programs to perform these calculations. In this paper we present an open source program written in Python 3.2 that performs calculations for standard Bonferroni, Bonferroni-Holm and Benjamini-Hochberg corrections.
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spelling pubmed-32630242012-01-24 An open-source software program for performing Bonferroni and related corrections for multiple comparisons Lesack, Kyle Naugler, Christopher J Pathol Inform Technical Note Increased type I error resulting from multiple statistical comparisons remains a common problem in the scientific literature. This may result in the reporting and promulgation of spurious findings. One approach to this problem is to correct groups of P-values for “family-wide significance” using a Bonferroni correction or the less conservative Bonferroni-Holm correction or to correct for the “false discovery rate” with a Benjamini-Hochberg correction. Although several solutions are available for performing this correction through commercially available software there are no widely available easy to use open source programs to perform these calculations. In this paper we present an open source program written in Python 3.2 that performs calculations for standard Bonferroni, Bonferroni-Holm and Benjamini-Hochberg corrections. Medknow Publications & Media Pvt Ltd 2011-12-26 /pmc/articles/PMC3263024/ /pubmed/22276243 http://dx.doi.org/10.4103/2153-3539.91130 Text en Copyright: © 2011 Lesack K. http://creativecommons.org/licenses/by-nc-sa/3.0 This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Technical Note
Lesack, Kyle
Naugler, Christopher
An open-source software program for performing Bonferroni and related corrections for multiple comparisons
title An open-source software program for performing Bonferroni and related corrections for multiple comparisons
title_full An open-source software program for performing Bonferroni and related corrections for multiple comparisons
title_fullStr An open-source software program for performing Bonferroni and related corrections for multiple comparisons
title_full_unstemmed An open-source software program for performing Bonferroni and related corrections for multiple comparisons
title_short An open-source software program for performing Bonferroni and related corrections for multiple comparisons
title_sort open-source software program for performing bonferroni and related corrections for multiple comparisons
topic Technical Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3263024/
https://www.ncbi.nlm.nih.gov/pubmed/22276243
http://dx.doi.org/10.4103/2153-3539.91130
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