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Biostatistics Series Module 3: Comparing Groups: Numerical Variables

Numerical data that are normally distributed can be analyzed with parametric tests, that is, tests which are based on the parameters that define a normal distribution curve. If the distribution is uncertain, the data can be plotted as a normal probability plot and visually inspected, or tested for n...

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Autores principales: Hazra, Avijit, Gogtay, Nithya
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Medknow Publications & Media Pvt Ltd 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4885176/
https://www.ncbi.nlm.nih.gov/pubmed/27293244
http://dx.doi.org/10.4103/0019-5154.182416
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author Hazra, Avijit
Gogtay, Nithya
author_facet Hazra, Avijit
Gogtay, Nithya
author_sort Hazra, Avijit
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description Numerical data that are normally distributed can be analyzed with parametric tests, that is, tests which are based on the parameters that define a normal distribution curve. If the distribution is uncertain, the data can be plotted as a normal probability plot and visually inspected, or tested for normality using one of a number of goodness of fit tests, such as the Kolmogorov–Smirnov test. The widely used Student's t-test has three variants. The one-sample t-test is used to assess if a sample mean (as an estimate of the population mean) differs significantly from a given population mean. The means of two independent samples may be compared for a statistically significant difference by the unpaired or independent samples t-test. If the data sets are related in some way, their means may be compared by the paired or dependent samples t-test. The t-test should not be used to compare the means of more than two groups. Although it is possible to compare groups in pairs, when there are more than two groups, this will increase the probability of a Type I error. The one-way analysis of variance (ANOVA) is employed to compare the means of three or more independent data sets that are normally distributed. Multiple measurements from the same set of subjects cannot be treated as separate, unrelated data sets. Comparison of means in such a situation requires repeated measures ANOVA. It is to be noted that while a multiple group comparison test such as ANOVA can point to a significant difference, it does not identify exactly between which two groups the difference lies. To do this, multiple group comparison needs to be followed up by an appropriate post hoc test. An example is the Tukey's honestly significant difference test following ANOVA. If the assumptions for parametric tests are not met, there are nonparametric alternatives for comparing data sets. These include Mann–Whitney U-test as the nonparametric counterpart of the unpaired Student's t-test, Wilcoxon signed-rank test as the counterpart of the paired Student's t-test, Kruskal–Wallis test as the nonparametric equivalent of ANOVA and the Friedman's test as the counterpart of repeated measures ANOVA.
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spelling pubmed-48851762016-06-10 Biostatistics Series Module 3: Comparing Groups: Numerical Variables Hazra, Avijit Gogtay, Nithya Indian J Dermatol IJD® Module on Biostatistics and Research Methodology for the Dermatologist - Module Editor: Saumya Panda Numerical data that are normally distributed can be analyzed with parametric tests, that is, tests which are based on the parameters that define a normal distribution curve. If the distribution is uncertain, the data can be plotted as a normal probability plot and visually inspected, or tested for normality using one of a number of goodness of fit tests, such as the Kolmogorov–Smirnov test. The widely used Student's t-test has three variants. The one-sample t-test is used to assess if a sample mean (as an estimate of the population mean) differs significantly from a given population mean. The means of two independent samples may be compared for a statistically significant difference by the unpaired or independent samples t-test. If the data sets are related in some way, their means may be compared by the paired or dependent samples t-test. The t-test should not be used to compare the means of more than two groups. Although it is possible to compare groups in pairs, when there are more than two groups, this will increase the probability of a Type I error. The one-way analysis of variance (ANOVA) is employed to compare the means of three or more independent data sets that are normally distributed. Multiple measurements from the same set of subjects cannot be treated as separate, unrelated data sets. Comparison of means in such a situation requires repeated measures ANOVA. It is to be noted that while a multiple group comparison test such as ANOVA can point to a significant difference, it does not identify exactly between which two groups the difference lies. To do this, multiple group comparison needs to be followed up by an appropriate post hoc test. An example is the Tukey's honestly significant difference test following ANOVA. If the assumptions for parametric tests are not met, there are nonparametric alternatives for comparing data sets. These include Mann–Whitney U-test as the nonparametric counterpart of the unpaired Student's t-test, Wilcoxon signed-rank test as the counterpart of the paired Student's t-test, Kruskal–Wallis test as the nonparametric equivalent of ANOVA and the Friedman's test as the counterpart of repeated measures ANOVA. Medknow Publications & Media Pvt Ltd 2016 /pmc/articles/PMC4885176/ /pubmed/27293244 http://dx.doi.org/10.4103/0019-5154.182416 Text en Copyright: © 2016 Indian Journal of Dermatology http://creativecommons.org/licenses/by-nc-sa/3.0 This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as the author is credited and the new creations are licensed under the identical terms.
spellingShingle IJD® Module on Biostatistics and Research Methodology for the Dermatologist - Module Editor: Saumya Panda
Hazra, Avijit
Gogtay, Nithya
Biostatistics Series Module 3: Comparing Groups: Numerical Variables
title Biostatistics Series Module 3: Comparing Groups: Numerical Variables
title_full Biostatistics Series Module 3: Comparing Groups: Numerical Variables
title_fullStr Biostatistics Series Module 3: Comparing Groups: Numerical Variables
title_full_unstemmed Biostatistics Series Module 3: Comparing Groups: Numerical Variables
title_short Biostatistics Series Module 3: Comparing Groups: Numerical Variables
title_sort biostatistics series module 3: comparing groups: numerical variables
topic IJD® Module on Biostatistics and Research Methodology for the Dermatologist - Module Editor: Saumya Panda
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4885176/
https://www.ncbi.nlm.nih.gov/pubmed/27293244
http://dx.doi.org/10.4103/0019-5154.182416
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