Friedman Test

The Friedman test is used to detect any difference between subjects under test measured variously multiple times. More precisely, this non-parametric test states whether there is a significant difference in the location parameters of k statistical samples (>= 3, columns candidates, treatments, subject), measured n times (rows, blocks, participants, measures), or not. The data in each row is ranked, based on which a resulting test statistic Q is calculated.

If n > 15 or k > 4, the test statistic Q can be approximated to be Χ2 distributed. With the given significance level α, a corresponding p-value (null hypothesis H0: there is no difference of the location parameters in the samples, alternative hypothesis HA: the samples in the columns have different location parameters) can be given.

Please refer also to the Wikipedia description of the Friedman Test.


The samples (columns) included in the test
Significance level α
A difference in the samples is assumed based on this value, 0 < α < 1

Advanced Settings

Missing Values Strategy
How missing values are going to be handled when the ranking is done:
  • MINIMAL - missing values are considered minimal in the ordering
  • MAXIMAL - missing values are considered maximal in the ordering
  • FIXED - missing values are left in place
  • FAILED - missing values result in Node failure
Ties Strategy
How ties in each block are going to be handled when the ranking is done:
  • SEQUENTIAL - Ties assigned sequential ranks in order of occurrence
  • MINIMUM - Ties get the minimum applicable rank
  • MAXIMUM - Ties get the maximum applicable rank
  • AVERAGE - Ties get the average of applicable ranks
  • RANDOM - Ties get a random integral value from among applicable ranks

Input Ports

The table from which to test samples

Output Ports

Friedman test evaluation


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