MULTIDIMENSIONAL TESTS

The tests implemented in this tool are used to compare samples described by several variables. For example, instead of comparing the average of two samples as with the Student t test, we compare here simultaneously for the same samples averages measured for several variables.

Compared to a procedure that would involve as many Student t tests as there are variables, the method proposed here has the advantage of using the structure of covariance of the variables and of obtaining an overall conclusion. It may be that two samples are different for a variable with a Student t test, but that overall it is impossible to reject the hypothesis that they are similar.

The tests are based on the Mahalanobis distance, the Wilks' Lambda statistic, the Box test and the Kullback's test.

Tutorial:

• How do I know if 2 samples or more described by several variables are significantly different or not?