The Friedman ANOVA is a nonparametric test that compares three or more paired groups by determining the score difference between k treatments of n blocks. It performs a nonparametric analysis of a randomized block experiment and thus provides an alternative to the one-way repeated measure ANOVA.
This test requires data to follow a balanced design, i.e. exactly one observation per treatment-block combination.
The hypothesis under test, , often called the null hypothesis, is that the k samples come from the same population, and this is to be tested against an alternative hypothesis that they come from different populations.
Friedman ANOVA does not support data with missing values. Please trim your missing values from data and make sure the data follow balanced design before running Friedman ANOVA
To perform a Friedman ANOVA:
Topics covered in this section: