I want to compare proportion data (sex ratio) between two treatments. There are 19 populations for each treatment (i.e., 19 replicates).
The specifics of the data is that it is the proportion of male offspring (i.e., sex ratio) resulting from from two treatments a and b. The experiment was repeated 19 times, though the samples are independent (i.e., not paired). The null hypothesis is that the proportion of male offspring (sex ratio) is not different between the two treatments.
I therefore have a nominal response variable (i.e., proportion male) and a categorical explanatory variable (treatment a or b).
My data table is set up as:
Treatment / male offspring / female offspring a / 17 / 54 a / 21 / 64 etc... (19 lines for a) b / 34 / 56 b / 45 / 57 etc.. (19 lines for b)
I have got as far as thinking that I need to use either a Pearson's chi-squared or a Fisher's exact test. I am not sure if that is the correct approach? I am also not sure how to analyse the data where there are replicates. For example, do I need to combine all the figures from the replicates to put into a 2x2 contingency table (i.e., total male and total female offspring in treatment a and total male and total female offspring in treatment b?)