Parametric hypothesis testing for comparison of two or more populations. Independent and dependent samples.

The tests for comparison of population try to determine if K (K >= 2) samples come from the same underlying population according to a variable of interest (X). We talk parametric test when we assume that the data come from a type of probability distribution. Thus, the inference relies on the parameters of the distribution. For instance, if we assume that the distribution of the data is Gaussian, the hypothesis testing relies on mean or on variance.

We handle univariate test in this tutorial i.e. we have only one variable of interest. When we want to analyze simultaneously several variables, we talk about multivariate test.

Keywords: t-test, F-Test, Bartlett's test, Levene's test, Brown-Forsythe's test, independent samples, dependent samples, paired samples, matched-pairs samples, anova, welch's anova, randomized complete blocks

Components: MORE UNIVARIATE CONT STAT, NORMALITY TEST, T-TEST, T-TEST UNEQUAL VARIANCE, ONE-WAY ANOVA, WELCH ANOVA, FISHER’S TEST, BARTLETT’S TEST, LEVENE’S TEST, BROWN-FORSYTHE TEST, PAIRED T-TEST, PAIRED V-TEST, ANOVA RANDOMIZED BLOCKS

Tutorial: en_Tanagra_Univariate_Parametric_Tests.pdf

Dataset: credit_approval.xls

References:

NIST/SEMATECH e-Handbook of Statistical Methods, http://www.itl.nist.gov/div898/handbook/ (Chapter 7, Product and Process Comparisons)