Friedman Test
Computes the Friedman test, the non-parametric version of repeated-measures ANOVA. It's used when the same subjects (or blocks) go through three or more conditions and you want to know whether the conditions differ, without assuming normality. Within each block, the treatments are ranked; if one treatment is consistently better, it accumulates low ranks across all blocks, and the Friedman chi-square statistic grows. Enter the blocks: treatments separated by commas, blocks by semicolons.
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Friedman Test
Computes the Friedman test, the non-parametric version of repeated-measures ANOVA. It's used when the same subjects (or blocks) go through three or more conditions and you want to know whether the conditions differ, without assuming normality. Within each block, the treatments are ranked; if one treatment is consistently better, it accumulates low ranks across all blocks, and the Friedman chi-square statistic grows. Enter the blocks: treatments separated by commas, blocks by semicolons.
Comparing several treatments on the same subjects
When the same individuals go through three or more conditions — three diets tested on each person, three methods rated by each judge — comparing the conditions requires accounting for the fact that the measurements come from the same subjects. Repeated-measures ANOVA does this, but it demands normality. The Friedman test is the alternative that drops that requirement.
The trick is to rank within each block. For each subject, the treatments are assigned positions according to their values, from smallest to largest. If a treatment is systematically better, it will collect low positions across almost every block, and the sum of its ranks will stand out from the rest. The Friedman chi-square statistic measures how far those rank sums depart from what would be expected if the conditions were equivalent.
Enter the blocks with treatments separated by commas and blocks separated by semicolons; each block needs the same number of treatments. The tool returns the chi-square statistic and the degrees of freedom. As with ANOVA, a significant result indicates that at least one treatment differs, without saying which; multiple comparisons such as Nemenyi are the next step.
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