% Kruskal_Wallis
% This is a statistical test to check two datasets if are different.
% Kruskal_Wallis is similar to one way ANOVA but the data is not expected
% to be natrual distribution. It works with any breaked condition of the
% ANOVA.
%
% As a reminder, the assumptions of the one-way ANOVA for independent samples are
% that the scale on which the dependent variable is measured has the properties of an equal interval scale;T
% that the k samples are independently and randomly drawn from the source population(s);T
% that the source population(s) can be reasonably supposed to have a normal distribution; andT
% that the k samples have approximately equal variances.
%
% We noted in the main body of Chapter 14 that we need not worry very much about the first, third, and fourth of these assumptions
% when the samples are all the same size. For in that case the analysis of variance is quite robust, by which we mean relatively
% unperturbed by the violation of its assumptions. But of course, the other side of the coin is that when the samples are not all
% the same size, we do need to worry. In this case, should one or more of assumptions 1, 3, and 4 fail to be met, an appropriate
% non-parametric alternative to the one-way independent-samples ANOVA can be found in the Kruskal-Wallis Test.
%
% see http://faculty.vassar.edu/lowry/ch14a.html
%
% Assumption used in the programming of this code is thatb oth data sets are equal