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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Vydáno v:Indian journal of dermatology Ročník 61; číslo 3; s. 251 - 260
Hlavní autoři: Hazra, Avijit, Gogtay, Nithya
Médium: Journal Article
Jazyk:angličtina
Vydáno: India Wolters Kluwer - Medknow Publications 01.05.2016
Medknow Publications and Media Pvt. Ltd
Medknow Publications & Media Pvt. Ltd
Medknow Publications & Media Pvt Ltd
Wolters Kluwer Medknow Publications
Témata:
Analysis
Analysis of variance
Biometry
Conflicts of interest
Dermatology
Economic models
Friedman's test
IJD
Independent sample
Kolmogorov–Smirnov test
Kruskal–Wallis test
Mann–Whitney U-test
Medical statistics
Module on Biostatistics and Research Methodology for the Dermatologist - Module Editor: Saumya Panda
Normal distribution
normal probability plot
Population
t-test
Tukey's test
Values
Wilcoxon's test
India
Tukey's test
Friedman's test
Kolmogorov-Smirnov test
Mann-Whitney U-test
t-test
Wilcoxon's test
Kruskal-Wallis test
Analysis of variance
normal probability plot
Kruskal–Wallis test
Kolmogorov–Smirnov test
Mann–Whitney U-test
ISSN:0019-5154, 1998-3611
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Abstract 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.
AbstractList 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.
Audience Academic
Author Gogtay, Nithya
Hazra, Avijit
AuthorAffiliation From the Department of Pharmacology, Institute of Postgraduate Medical Education and Research, Kolkata, West Bengal, India
1 Department of Clinical Pharmacology, Seth GS Medical College and KEM Hospital, Parel, Mumbai, Maharashtra, India
AuthorAffiliation_xml – name: 1 Department of Clinical Pharmacology, Seth GS Medical College and KEM Hospital, Parel, Mumbai, Maharashtra, India
– name: From the Department of Pharmacology, Institute of Postgraduate Medical Education and Research, Kolkata, West Bengal, India
Author_xml – sequence: 1
  givenname: Avijit
  surname: Hazra
  fullname: Hazra, Avijit
  organization: Department of Pharmacology, Institute of Postgraduate Medical Education and Research, Kolkata, West Bengal
– sequence: 2
  givenname: Nithya
  surname: Gogtay
  fullname: Gogtay, Nithya
  organization: Department of Clinical Pharmacology, Seth GS Medical College and KEM Hospital, Parel, Mumbai, Maharashtra
BackLink https://www.ncbi.nlm.nih.gov/pubmed/27293244$$D View this record in MEDLINE/PubMed
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ContentType Journal Article
Copyright COPYRIGHT 2016 Medknow Publications and Media Pvt. Ltd.
Copyright Medknow Publications & Media Pvt Ltd May-Jun 2016
Copyright: © 2016 Indian Journal of Dermatology 2016
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Issue 3
Keywords Tukey's test
Friedman's test
Kolmogorov-Smirnov test
Mann-Whitney U-test
t-test
Wilcoxon's test
Kruskal-Wallis test
Analysis of variance
normal probability plot
Kruskal–Wallis test
Kolmogorov–Smirnov test
Mann–Whitney U-test
Language English
License 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.
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wolterskluwer_medknow_10_4103_0019-5154_182416_251_Biostati
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PublicationTitle Indian journal of dermatology
PublicationTitleAlternate Indian J Dermatol
PublicationYear 2016
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Snippet 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...
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StartPage 251
SubjectTerms Analysis
Analysis of variance
Biometry
Conflicts of interest
Dermatology
Economic models
Friedman's test
IJD
Independent sample
Kolmogorov–Smirnov test
Kruskal–Wallis test
Mann–Whitney U-test
Medical statistics
Module on Biostatistics and Research Methodology for the Dermatologist - Module Editor: Saumya Panda
Normal distribution
normal probability plot
Population
t-test
Tukey's test
Values
Wilcoxon's test
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Title Biostatistics series module 3: Comparing groups: Numerical variables
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