I am trying to figure out the best way to test my data for differences in R. The data I have look like this:
domain variable lifecycle
162 CSP1 5
162 old Species 10
162 BD 16
162 DBH 14
162 Height 16
162 Height comments 16
162 Other comments 4
162 Year 5
123 species 12
123 den bran 14
123 den core 14
191 CSP2 4
191 C kg ha 5
185 location 5
185 biomass 6
82 CSP num 6
82 depth lb 8
82 C t 7
190 Layer 8
190 Dry weight 9
204 item plot 5
204 item volume 7
204 within central plot small 7
205 plot 3
205 successional stage 3
205 rarefy hundred 3
Now I want to use the "variable" column as factorial group and the "lifecycle" as the characteristiom to compare. Because the data is not normally distributed and I have more than two groups to compare I chose a Kruskal-Wallis test, which shows no significant differences.
Kruskal-Wallis rank sum test
data: lifecycle by variable
Kruskal-Wallis chi-squared = 25, df = 25, p-value = 0.4624
My question is now if I can use the Kruskal-Wallis in this case to find differences in "lifecycle" for the "variable" groups, or is the sample size to small with only one value per group?
And if it is not usable here in this case what would be a better solution?
EDIT:
I did the Kruskal-Wallis now with the domain as grouping factor. Which gives me a significant difference. The interpretation would be that there are domains with significant more or less impact than other.
After that I performed a post-hoc test to see between which of the domains the differences occur. But supprisingly the paired wilcoxon test with bonferroni correction showed up no significant grup differences. How could this be?