# 1 -Introduction to hypothesis testing

Hypothesis tests make it possible to compare one or more samples, and to validate or invalidate a hypothesis.

# 2 – The fit test

Adjustment tests identify the law that our data follows.

# 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.

# Wilcoxon-Mann Whitney test

The Wilcoxon – Mann Whitney test allows you to compare performance levels.

# 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.

# Kolmogorov-Smirnov Test

The main interest of the Kolmogorov Smirnov test is to process ordinal 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.