I have been working with a dataset that contains a pair of control/ test variable that represents the anthropization effect on water quality. This dataset was obtained through a literature systematic review, in which I reunited published data that showed natural and anthropized (impacted) environments. My main issue in analysing this dataset is that the data are not normally distributed, and my samples are not independent, since I have registers of before/ after impact combined with situations where the "control" environment was used as a reference for two different impacts, generating a dependence of the same "controls" for two results at the other column. In this case, neither my rows nor columns are completely independent and at this point, I do not have enough info to separate the dataset.
I have been looking for nonparametric solutions to compare the means of the two groups but from what I have found Wilcoxon and Kruskal- Wallis tests assume that the data are independent.
Is there a test that suits my goal?
I have been looking for nonparametric solutions to compare the means of the two groups but from what I have found Wilcoxon and Kruskal- Wallis tests assume that the data are independent.
Is there a test that suits my goal?