I'm looking at the association between two categorical variables in a genus of birds. The variables are 'Conservation Concern' (Yes/No) and another binary variable (Yes/No) and I have these for every species in the genus. I know there may be issues with the variables (i.e. have they been adequately described) but they are what they are. I've calculated odds ratios but I'm not sure if I'm supposed to do a statistical test to show they are different.
When I run a statistical test (e.g. a Fisher's exact test) I get an odds ratio of 6 (agreeing with my hand calculation) and a p-value of 0.11. This means I can't say there is a significant difference in conservation status between the two groups. But the point of tests like Fisher's exact test is to tell you whether the odds ratio is different from 1, because they are usually used to infer things about a greater population, but I'm not trying to estimate a value as I know what it is, and it's 6. However, I suspect I should instead just be interpreting this as "I can't say this difference isn't due to chance".
I've looked at this question but am still unsure, mainly because that example involves a changing population.
I am wary of reporting results without supporting them with statistical analyses. But I also think it is weird to discount a known true value. I therefore don't know whether or not it's appropriate to use a statistical test in this case to make inferences about my data.