I have turning data for animals in different conditions with comparisons of before and after these conditions, we'll say condA condB.
Each animal has 3 repeats on both conditions, where before a treatment is compared to after a treatment (of the same condition). So condA before is compared to condA after, etc.
Is the best approach to find the mean/median (depending on normality of data) of each of the repeats and then performing a paired T-test/Mann-Whitney (depending on the normality of the means) to compare the means of each condition?
Without the variance of the original data, is the variance eliminated? Or is the variance between the 6 means/medians (3 before, 3 after) enough?
Is it also correct to use a median when the data are not parametric, mean when data are parametric?
Edit for formatting:
Thank you for this. So would this be correct in lmer:
Response variables = turning rate under all conditions, gender, length(of animal)
Random affect = Animal ID
lmer(condABefore ~ gender + length + (1| animalID))
lmer(condAAfter ~ gender + length + (1| animalID))
lmer(condBBefore ~ gender + length + (1| animalID))
lmer(condBAfter ~ gender + length + (1| animalID))