The Friedman test is a non-parametric statistical test developed by the U.S. economist Milton Friedman. Similar to the parametric repeated measures ANOVA, it is used to detect differences in treatments across multiple test attempts. The procedure involves ranking each row (or block) together, then considering the values of ranks by columns. Applicable to complete block designs, it is thus a special case of the Durbin test. The Friedman test is used for two-way repeated measures analysis of variance by ranks. In its use of ranks it is similar to the Kruskal-Wallis one-way analysis of variance by ranks. When the number of blocks or treatments is large the probability distribution can be approximated by chi-square or F distribution. If n or k is small, the approximation to chi-square becomes poor and the p-value should be obtained from tables of Q specially prepared for the Friedman test. The MatLab function FRIEDMAN only uses the chi-square approximation. On the contrary, MYFRIEDMAN uses the exact distribution for small size samples and chi-square and F distribution for large sample size. If the p-value is significant, a post-hoc multiple comparisons.
Created by Giuseppe Cardillo
To cite this file, this would be an appropriate format: Cardillo G. (2009). MYFRIEDMAN: Friedman test for non parametric two way ANalysis Of VAriance http://www.mathworks.com/matlabcentral/fileexchange/25882
Giuseppe Cardillo (2023). MyFriedman (https://github.com/dnafinder/myfriedman), GitHub. Retrieved .
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more critical values for small samples; input parser; table implementation; github link
bug correction for small samples computation
bug fixed in input error handling section
Multiple post-hoc comparisons test added
Changes in description
Change in help section to cite this file