Some small violations may have little practical effect on the analysis, while other violations may render the Friedman's test result uselessly incorrect or uninterpretable. In particular, small or unbalanced sample sizes can increase vulnerability to assumption violations. If the data are in a particular order, consider the possibility of dependence. Note that because data for Friedman's test is often arranged systematically with respect to the blocking factor as well as the treatment factor, it may be difficult to detect an implicit factor in the original data this way. Values may not be identically distributed because of the presence of outliers .
Outliers are anomalous values in the data.
They may be due to recording errors, which may be correctable, or they may be due to the sample not being entirely from the same population. Apparent outliers may also be due to the values being from the same, but skewed or heavy-tailed population. Outliers tend to increase the estimate of sample variation, and might lead to an incorrect conclusion that the dispersions or variances of the samples are not equal if the outlier is the result of a recording or measurement error. Because the statistic for Friedman's test is resistant , it will not be substantially affected by the presence of outliers unless the number of outliers becomes large relative to the sample size. If you find outliers in your data that are not due to correctable errors, you may wish to consult a statistician as to how to proceed.