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The Non-parametric Friedman Test
The Non-parametric Friedman Test
The Friedman test is a non-parametric test used to test for differences between groups when the dependent variable is at least ordinal (could be continuous). The Friedman test is the non-parametric alternative to the one-way ANOVA with repeated measures (or the complete block design and a special case of the Durbin test). If the data is significantly different than normally distributed this becomes the preferred test over using an ANOVA.
The test procedure ranks each row (block) together, then considers the values of ranks by columns. The data is organized in to a matrix with B rows (blocks) and T columns (treatments) with a single operation in each cell of the matrix.
Assumptions
As with nearly any statistical test, there are assumptions to consider. Here let’s illuminate four elements to consider:
- There is one group of test subjects that are measured on three or more different occasions.
- The group is a random sample from the population.
- The dependent variable is at least an ordinal or continuous (Likert scales, time, intelligent, percentage correct, etc.)
- The samples need not be normally distributed.
