A number of parametric and non-parametric statistical tests can be used to determine if identified spatial relationships are considered statistically significant.

In the analysis of designed experiments, the Friedman test is the most common non-parametric test for complete block designs.

Cochran's Q test, a non-parametric test that is applied to the analysis of two-way randomized block designs with a binary response variable.

Frank Wilcoxon, chemist and statistician, inventor of two non-parametric tests for statistical significance:

Further, these analyses relied upon non-parametric tests at the level of the school.

Results were corroborated by non-parametric tests.

The following tables provide guidance to the selection of the proper parametric or non-parametric statistical tests for a given data set.

Both parametric and non-parametric tests were used because the distributional assumptions required for parametric testing may not be satisfied in all cases.

If this normality assumption is not valid, an alternative is to use a non-parametric test.

The most common non-parametric test for the one-factor model is the Kruskal-Wallis test.