Timeline for Group comparison (and pairwise tests) with non-independent data
Current License: CC BY-SA 4.0
13 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| S Oct 6 at 14:46 | vote | accept | Barcik | ||
| S Oct 6 at 14:46 | vote | accept | Barcik | ||
| S Oct 6 at 14:46 | |||||
| Oct 4 at 14:23 | answer | added | EdM | timeline score: 3 | |
| Oct 4 at 6:22 | comment | added | BenP | @Barcick Ah, now I understand your data better! So, from each quadrant you took a number of. samples, probably more than one. Each sample leads to a percentage. That makes summing frequencies less meaningful, as I suggested first. Means are better then. I find this not irrelevant for your question, maybe add it for future readers.Thanks for explaining. | |
| Oct 3 at 13:28 | comment | added | Barcik | @BenP I didn't explained exactly what is the variable abundance because I deemed irrelevant for Cross Validated. But here it is: in each quadrant I collect soil samples; each soil sample have many fungi, so the technique I used detects the number of DNA snippets (counts) for each species. The abundance is the number of DNA counts of a species divided by the total DNA count. So, each soil sample is a representation of the fungi that exists in the soil. I chose quadrants to get a good representation of area of the plot (which is big: a 30m radius circle in a forest). | |
| Oct 3 at 10:32 | vote | accept | Barcik | ||
| S Oct 6 at 14:46 | |||||
| Oct 2 at 17:51 | comment | added | BenP | @Barcik I do not understand why you made 4 quadrants per plot. If you count the abundance (absolute frequency) in each quadrant, you can also sum these across the four quadrants to obtain the total abundance per plot and of course also the relative abundance. And this, say "total", abundance is a better measure for the given plot than each of the four quadrants abundances. No need to take averages, just sum and divide by N in the plot. Or: forget quadrants. | |
| Oct 1 at 19:41 | answer | added | jginestet | timeline score: 2 | |
| Oct 1 at 19:30 | comment | added | Barcik | Thank you @EdM, I edited it: the abundance refers to the relative abundance of a species X over the total abundance of all species. And it's not normal because it has many small values compared to larger values (inverted J). | |
| Oct 1 at 19:28 | history | edited | Barcik | CC BY-SA 4.0 | added 274 characters in body |
| Oct 1 at 17:58 | comment | added | EdM | Please edit the question to explain how you measure the Abundance: that is, what constitutes the numerator and the denominator in the percentage calculation. Also, please edit to clarify what aspect of the distribution is "not normal." It's not a problem if there isn't normality among all the outcome observations, as the treatment presumably affects the outcomes. Even within-treatment non-normality of outcomes isn't necessarily a problem, however. Depending on how you calculate the percentages, there might be a good way to deal with the "non-normality" with a different type of model. | |
| Oct 1 at 17:45 | history | edited | EdM | CC BY-SA 4.0 | deleted 47 characters in body |
| Oct 1 at 17:08 | history | asked | Barcik | CC BY-SA 4.0 |