Using the K-S-Test, one can determine whether two data sets
are drawn from the same distribution. The function performs a significance test and compares the cumulative distribution functions of the given signals xA and xB, and calculates the maximum distance D between them.
What makes the K-S-test very useful is its invariance under
reparametrization of x, i.e. the x axis can be locally stretched or
shifted without changing D.
The results of "Kolmogorov-Smirnov test", applied to two signals xA and xB, will appear
in the Dataplore ® message window.
Return parameters of "Kolmogorov-Smirnov test" are:
- the maximum distance D
- the significance signif, i.e. the probability [0..1] of randomly equal distributions, where small values indicate significant differences in the cumulative distribution functions.
BDS test, Test for different histograms,
Test for different means, Test for different variances,
Test for non-normal-distributed data,
Test for non-white-noise,
Test for the mean of a Gaussian, Test for the variance of a Gaussian,
Hollander/Wolfe , Lehmann