I already looked around quite a bit but didn't find answers (I understood), so I apologise if this question should be a duplicate.
I'm analysing a small cohort (n=15) of people with some observed difference/outcome
X, which is either positive or negative.
I want to check whether the number of positive
X is different between one or many of three binary categories:
Older than Y?,
Born in Country Z?, and
My naive approach was to construct three 2x2 contingency tables, do a Fisher's Exact test on each, and apply Bonferroni correction by multiplying the p-values times 3 (since I tested
X vs Age,
X vs Country,
X vs Children).
So my question, as I'm not super statistically fluid: Is this correct? Are there better ways which also might take into account that the variables might be dependent on another (e.g. age and previous children)? I can't really combine the three variables into one since my sample is so small.