For a set of data that I collected, I used the Kruskal-Wallace test to determine whether there were between-group differences in the parameter I measured. It returned a significant result, so in order to determine pairwise differences between my five treatment groups, I applied Dunn's test for multiple comparisons, which yielded these results:
I would normally use a cut off of p<0.05 to determine significance, but because of the multiple comparisons I used a Bonferroni correction. Five groups were compared (AB, Control, SM, DSV, and SW), resulting in ten pairwise comparisons. Thus I assessed significant when p was less than 0.05/10(=0.005). For the table a * indicates significance at the <0.05 level, and ** at the corrected (<0.005) level.
I can tell from the table which groups have statistically different rank means from one another (i.e. the groups are different).
My question: is it appropriate to evaluate relative between group differences based on the magnitude of the p-values? For example, is it appropriate to say, based on the data provided, that the AB-Control groups are the most similar to one another? And, Control-SM have the second highest degree of similarity out of all the comparisons made here?