I have a test design in which I have 1 base condition and around 14 test conditions. Each of those 15 conditions has a total of 15 observation points (so equal sample size). The base condition represents the data points in the original sample, whereas the test conditions all represent the data points after a different intervention. The data is continuous.
Now I would like to compare all conditions with each other, so that I can end up knowing which intervention was the most successful, essentially a ranking.
Most likely, the distributions of the conditions are not normal and variances will differ across the conditions, because of this a simple ANOVA cannot suffice.
I am now thinking that the best solution might be a non-parametric test like the Wilcoxon or Kruskall-Wallis, however, I would prefer it if there is a parametric option viable.
Which test is appropriate in this situation?