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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
Descripción
Sumario: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.