Description |
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 (i.e. n > 15 or k > 4), the probability distribution can be approximated by that of a chi-square 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; else the F and chi square distributions If the p-value is significant, appropriate post-hoc multiple comparisons test is performed
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