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3 – Homoscedaticity tests

Used to test the differences in the variances between samples, they allow in particular to ensure the conditions of use of the hypothesis tests.

Fisher Snedecor Test

The Fisher-Snedecor test makes it possible to compare the variance of 2 samples.

one-way ANOVA

The analysis of the variance allows to study the average of K samples.

McNemar Test

The McNemar test allows to compare 2 populations that can only take 0 or 1:0 values being the non presence of a character, with 1 being the presence of a character.

Paired T test

The test is used to compare sample average with paired data.

Student T-Test

The student test allows to identify differences on average or proportional parameters.

Khideux Test

The χ2 test tests the adequacy of a data distribution with expected law or other data distributions.

Q of Cochran

Cochran’s Q is a generalization of the McNemar test and allows to process more than 2 sets of matched data.

Normal probability plot

The normal probability Plot is probably the simplest test of normality. Everyone will be able to use it to determine the “normality” of its data.

Kruskal and Wallis Test

The Kruskal and Wallis test is a non-parametric test for comparing more than 2 samples on data that can be averages, frequencies, or variances.

Wilcoxon Test

The Wilcoxon test is not to be confused with the Wilcoxon-Mann Whitney test. Even if they are similar, this one is suitable for matched data.

Anova in blocks

The Block Anova allows you to study more than 2 paired samples.

The Friedman test

The Friedman test is a generalization of the Wilcoxon test for more than 2 samples.

The Spearman Rho

The Spearman Rho allows to detect a correlation or not between variable.

Kendall’s Tau

Called the Kendall rank correlation coefficient, it is a non-parametric correlation measure. It is used to determine a relationship between two sets of data.

Durbin Watson test

The Durbin Watson Test is used as part of a regression study to determine whether the data is independent.

Post-HOC Tests

Very many, these are tests that allow to identify the samples that differ among the X that we tested beforehand.

Anderson Darling Test

It allows us to test whether our data follow a diverse law, especially the normal law.

Shapiro-Wilk Test

The Shapiro Wilk test is effective at validating normality when we have less than 50 data.

Lilliefors Test

The Lilliefors test is a variation of the Kolmogorov Smirnov test to test normality

Brown Forsythe Test

The Brown Forsythe test is the best test to compare the variance of 2 or more samples.

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