I'm trying to look across a dozen or so groups of cases with respect to a condition they may have or not. I have a predetermined expected frequency for each condition status (c1 & c2, let's assume yes/no)
Now, I'm performing a simple test of significance between the observed and expected values per group as if each were a 2x2 contingency table. In cases where all values are above 5, I perform a chi-square test and all other cases I perform Fisher's exact test of significance.
Is it incorrect to perform different tests per group? I'm not necessarily interested in comparing between groups but rather trying to determine if the difference between c1 and c2 is significant from what would be expected in each condition per group.
Would it make more sense just to use Fisher's exact test on everything?
Here's a mock representation of my data. group1 and 3 use Fisher's test as there are some values below 5, while groups 2 and 4 use Chi-square.
c1.obs c2.obs c1.exp c2.exp p.value group1 3 21 2 22 1 group2 9 48 7 60 5.5E-7 group3 13 19 3 29 8.1E-03 group4 34 182 22 194 4.9E-03 ...