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To test or not to test: Preliminary assessment of normality when comparing two independent samples

BACKGROUND: Student’s two-sample t test is generally used for comparing the means of two independent samples, for example, two treatment arms. Under the null hypothesis, the t test assumes that the two samples arise from the same normally distributed population with unknown variance. Adequate contro...

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Autores principales: Rochon, Justine, Gondan, Matthias, Kieser, Meinhard
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3444333/
https://www.ncbi.nlm.nih.gov/pubmed/22712852
http://dx.doi.org/10.1186/1471-2288-12-81
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author Rochon, Justine
Gondan, Matthias
Kieser, Meinhard
author_facet Rochon, Justine
Gondan, Matthias
Kieser, Meinhard
author_sort Rochon, Justine
collection PubMed
description BACKGROUND: Student’s two-sample t test is generally used for comparing the means of two independent samples, for example, two treatment arms. Under the null hypothesis, the t test assumes that the two samples arise from the same normally distributed population with unknown variance. Adequate control of the Type I error requires that the normality assumption holds, which is often examined by means of a preliminary Shapiro-Wilk test. The following two-stage procedure is widely accepted: If the preliminary test for normality is not significant, the t test is used; if the preliminary test rejects the null hypothesis of normality, a nonparametric test is applied in the main analysis. METHODS: Equally sized samples were drawn from exponential, uniform, and normal distributions. The two-sample t test was conducted if either both samples (Strategy I) or the collapsed set of residuals from both samples (Strategy II) had passed the preliminary Shapiro-Wilk test for normality; otherwise, Mann-Whitney’s U test was conducted. By simulation, we separately estimated the conditional Type I error probabilities for the parametric and nonparametric part of the two-stage procedure. Finally, we assessed the overall Type I error rate and the power of the two-stage procedure as a whole. RESULTS: Preliminary testing for normality seriously altered the conditional Type I error rates of the subsequent main analysis for both parametric and nonparametric tests. We discuss possible explanations for the observed results, the most important one being the selection mechanism due to the preliminary test. Interestingly, the overall Type I error rate and power of the entire two-stage procedure remained within acceptable limits. CONCLUSION: The two-stage procedure might be considered incorrect from a formal perspective; nevertheless, in the investigated examples, this procedure seemed to satisfactorily maintain the nominal significance level and had acceptable power properties.
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spelling pubmed-34443332012-09-20 To test or not to test: Preliminary assessment of normality when comparing two independent samples Rochon, Justine Gondan, Matthias Kieser, Meinhard BMC Med Res Methodol Research Article BACKGROUND: Student’s two-sample t test is generally used for comparing the means of two independent samples, for example, two treatment arms. Under the null hypothesis, the t test assumes that the two samples arise from the same normally distributed population with unknown variance. Adequate control of the Type I error requires that the normality assumption holds, which is often examined by means of a preliminary Shapiro-Wilk test. The following two-stage procedure is widely accepted: If the preliminary test for normality is not significant, the t test is used; if the preliminary test rejects the null hypothesis of normality, a nonparametric test is applied in the main analysis. METHODS: Equally sized samples were drawn from exponential, uniform, and normal distributions. The two-sample t test was conducted if either both samples (Strategy I) or the collapsed set of residuals from both samples (Strategy II) had passed the preliminary Shapiro-Wilk test for normality; otherwise, Mann-Whitney’s U test was conducted. By simulation, we separately estimated the conditional Type I error probabilities for the parametric and nonparametric part of the two-stage procedure. Finally, we assessed the overall Type I error rate and the power of the two-stage procedure as a whole. RESULTS: Preliminary testing for normality seriously altered the conditional Type I error rates of the subsequent main analysis for both parametric and nonparametric tests. We discuss possible explanations for the observed results, the most important one being the selection mechanism due to the preliminary test. Interestingly, the overall Type I error rate and power of the entire two-stage procedure remained within acceptable limits. CONCLUSION: The two-stage procedure might be considered incorrect from a formal perspective; nevertheless, in the investigated examples, this procedure seemed to satisfactorily maintain the nominal significance level and had acceptable power properties. BioMed Central 2012-06-19 /pmc/articles/PMC3444333/ /pubmed/22712852 http://dx.doi.org/10.1186/1471-2288-12-81 Text en Copyright ©2012 Rochon et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License(http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Rochon, Justine
Gondan, Matthias
Kieser, Meinhard
To test or not to test: Preliminary assessment of normality when comparing two independent samples
title To test or not to test: Preliminary assessment of normality when comparing two independent samples
title_full To test or not to test: Preliminary assessment of normality when comparing two independent samples
title_fullStr To test or not to test: Preliminary assessment of normality when comparing two independent samples
title_full_unstemmed To test or not to test: Preliminary assessment of normality when comparing two independent samples
title_short To test or not to test: Preliminary assessment of normality when comparing two independent samples
title_sort to test or not to test: preliminary assessment of normality when comparing two independent samples
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3444333/
https://www.ncbi.nlm.nih.gov/pubmed/22712852
http://dx.doi.org/10.1186/1471-2288-12-81
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