  Statistical distances quantifies the difference between two statistical
constructs. In this article, we describe reference values for a distance
between samples derived from the Kolmogorov-Smirnov statistic $D_{F,F'}$. Each
measure of the $D_{F,F'}$ is a measure of difference between two samples. This
distance is normalized by the number of observations in each sample to yield
the $c'=D_{F,F'}\sqrt{\frac{n n'}{n+n'}}$ statistic, for which high levels
favor the rejection of the null hypothesis (that the samples are drawn from the
same distribution). One great feature of $c'$ is that it inherits the
robustness of $D_{F,F'}$ and is thus suitable for use in settings where the
underlying distributions are not known. Benchmarks are obtained by comparing
samples derived from standard distributions. The supplied example applications
of the $c'$ statistic for the distinction of samples in real data enables
further insights about the robustness and power of such statistical distance.
