In this example we have three groups of data contained in the numeric waves data1, data2 and data3. The values represent angles in radians:

The data are shown here in a polar plot:

Multi-sample testing for angular dispersion

  StatsAngularDistanceTest/T=1/Q/ANGM data1,data2,data3

The results of the test are shown in the "Kruskal-Wallis Test" table:

The H statistic is clearly below both the Chi-square and Wallace approximate critical values so the hypothesis H₀: no effective dispersion can't be rejected at the default 0.05 significance level. Note that in this case we have used the /ANGM flag to compute the relative dispersion of the data about each sample's mean instead of providing sample means using the /ANGW flag. Here the operation first computes the mean of each sample and then performs the Kruskal-Wallis test on the difference of each datum from its corresponding mean value.

Two-sample testing for angular dispersion

  StatsAngularDistanceTest/T=1/Q/ANGM data1,data2

The results of the test are shown in the "Mann-Whitney Wilcoxon Test" table:

As one would expect, we get a P-value which is much greater than alpha (0.05) so H₀: no dispersion can't be rejected.

Two-sample testing for angular dispersion with user-specified mean direction

  StatsAngularDistanceTest/T=1/Q/ANG={2.35619,2.35619} data1,data2

The results of the test are shown in the "Mann-Whitney Wilcoxon Test" table:

Clearly, the P-value is less than alpha and so we reject H₀.