Open AccessBiostatistics series module 3: Comparing groups: Numerical variables
Author(s) -
Avijit Hazra,
Nithya J Gogtay
Publication year - 2016
Publication title -
indian journal of dermatology/indian journal of dermatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.395
H-Index - 36
eISSN - 1998-3611
pISSN - 0019-5154
DOI - 10.4103/0019-5154.182416
Subject(s) - statistics , mathematics , normality , analysis of variance , normality test , goodness of fit , parametric statistics , population , sample size determination , normal distribution , data set , statistical hypothesis testing , medicine , environmental health
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.