I'm comparing ordinal variables (first is location, second is source) to the number of mites per bee. The problem is that several of the samples don't have mites; others have a TON of mites. My data isn't normal (right-skewed). So I decided to use a Kruskal Wallis test followed by a Dunn's test for pairwise analysis. Now, I do get significance, but my graph looks incredibly wonky and I don't think it is particularly useful to the viewer. I think since the majority of samples had no mites, the median is zero for all my groups. BUT there is a difference between groups. So my questions are: Do I just scrap this graph and use the table? Would changing the numbers to ranges help? that way the ranking process is easier? Please show a better display if you think there is one.
Lineage & mite load χ2(3) = 5.02, p = .170, indicating that mite loads were similar for each level of Lineage Source & mite load χ2(2) = 21.22, p < .001, indicating mite loads were significantly different between levels of Source Region & mite load χ2(2) = 69.51, p < .001, indicating mite loads were significantly different between levels of Region
|Score Mean Difference||SE||Z||p-value||Adj. p-value|
- 469 views
- Stats question
- Car accidents and the Poisson distribution
- One Way Anova
- Correlation of Normal Random Variables
- What plots should I use to describe the relationship between these 8 continuous variables?
- Conditional mean and variance for joint PDF
- Question about COEFFICIENT OF CORRELATION
- applied probability