Anyway, first post...

I have been asked to look at some stats by a colleague, and have hit a brick wall (at least in my head).

The lab involves up to 30 germinating seeds under a treatments of nutrient, all other conditions are controlled. The data I have are of biomass of each individual plant. There are 9 treatments/concentrations under test, and a total of 216 measurements (some did not germinate).

My colleague usually ploughs into ANOVA at this point. I have suggested he tests for heteroscedasticity. He has, and concluded that ANOVA was not appropriate. I confirmed this by plotting histograms and a QQ Normal plot. So, we move to Kruskal-Wallis.

The K-W test shows significance at our alpha (0.05). Due to there being unequal sample sizes, I then chose a Dunn's Test to compare groups to look for where this difference lies, using the Bonferroni adjustment for multiple comparisons.

The test suggests that no one group is significantly different from another.

Am I just looking at this issue incorrectly? It makes little sense that when I test the whole dataset there is a difference between groups, yet when I look for this difference it isn't there! Is this just an issue of one test looking at the dataset as a whole , and other looking at it in a piecemeal fashion?

Must confess, I am a bit stumped. I just need a simple-to-understand explanation which I can relay back to my colleague and, most importantly, the students - they're freaked out by K-W and having to do stuff 'long hand' on Excel as it is....this is causing much brain ache.

Any thoughts gladly received, and thanks, in advance...