2019
DOI: 10.21105/joss.01169
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scikit-posthocs: Pairwise multiple comparison tests in Python

Abstract: License Authors of papers retain copyright and release the work under a Creative Commons Attribution 4.0 International License (CC-BY). Summary scikit-posthocs is a Python package providing multiple pairwise comparison tests (post hocs). Statisticians, data scientists, and researchers will find it useful in a statistical analysis routine to assess the differences between group levels if a statistically significant result of a parametric or nonparametric analysis of variance (ANOVA) test has been obtained.

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Cited by 200 publications
(130 citation statements)
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“…Any mRNA that rejected the null hypothesis (p < 0.05) was subjected to a post-hoc Dunn's Multiple Comparison Test with Benjamini-Krieger-Yekutieli two-stage false discovery rate correction [77]. These comparisons were completed using the SciPy [78] and scikit-posthocs [79] Python packages. Statistical significance between two values was determined using a two-tailed t test using Prism 7 (GraphPad, San Diego, CA, USA).…”
Section: Resultsmentioning
confidence: 99%
“…Any mRNA that rejected the null hypothesis (p < 0.05) was subjected to a post-hoc Dunn's Multiple Comparison Test with Benjamini-Krieger-Yekutieli two-stage false discovery rate correction [77]. These comparisons were completed using the SciPy [78] and scikit-posthocs [79] Python packages. Statistical significance between two values was determined using a two-tailed t test using Prism 7 (GraphPad, San Diego, CA, USA).…”
Section: Resultsmentioning
confidence: 99%
“…A Kruskal-Wallis test was used to identify statistically significant differences across two or more groups, and a Mann-Whitney U test was used for pairwise tests using a Holm-Sidak correction for multiple hypothesis testing [71,72]. We used the scipy [73] stats implementation of the Kruskal-Wallis test and the scikit-learn post hoc processing [74] implementation of pairwise Mann-Whitney U tests. Spearman rank and Pearson correlation values were calculated using the scipy library [72].…”
Section: Resultsmentioning
confidence: 99%
“…Analysis of cell numbers and EAG responses including statistical comparison and plotting was performed using Python (version 3.7.3, Python Software Foundation, www.python.org) based custom scripts. Those scripts utilize the SciPy ecosystems (www.scipy.org) modules SciPy (version 1.2.1) 112 , Numpy (version 1.16.2) 113 , Matplotlib (version 3.0.3) 114 , and Pandas (version 0.24.2) 115 , as well as the additional modules scikit-posthocs (version 0.5.1, https://github.com/maximtrp/scikit-posthocs) 116 , Seaborn (version 0.9.0) 117 , and localreg (version 0.2.1, https://github.com/sigvaldm/localreg).…”
Section: Methodsmentioning
confidence: 99%