The tests of equal variability (or dispersion, or scale, or simply variance) are often presented as a preliminary test before the comparison of means, in order to verify the homoscedasticity assumption. But this is not their only purpose. Compare dispersions can be an end in itself. For example, we wish to compare the performance of two systems of heating. The average temperature at the center of the room is the same; however one can wish to compare the mode of diffusion of heat in different parts of the room.

The parametric tests are based primarily on the Gaussian distribution. The test becomes a test for homogeneity of variance. We highlight the Levene test in this tutorial. Other tests exist (Bartlett test for instance), we mention them in this tutorial.

When the normality assumption is questionable, when sample size is low, when the variable is ordinal and not continuous, it is more appropriate to use non parametric tests. These are called tests for equality of scales or dispersions. In fact the procedures are not based on estimated variances. We will use well known techniques such as the Ansari-Bradley test, the Mood or the Klotz test. They have a scope broader since nonparametric. Some of these tests have a drawback, they are not applicable when the distributions conditionals do not share the same parameter of central tendency (the median in general, but we can adjust the values by centering in relation to the median).

In this tutorial, we show how to implement these various tests with Tanagra.

**Keywords**: parametric test, non parametric test, independent samples, Levene test, Bartlett test, Brown-Forsythe test, Mood test, Klotz test, Ansari-Bradley test

**Components**: LEVENE’S TEST, ANSARI-BRADLEY SCALE TEST, MOOD SCALE TEST, KLOTZ SCALE TEST

**Tutorial**: en_Tanagra_Nonparametric_Test_for_Scale_Differences.pdf

**Dataset**: tests_for_scale_differences.xls

**References**:

NIST, "Quantitative techniques", section 1.3.5 - http://www.itl.nist.gov/div898/handbook/eda/section3/eda35.htm