My data is count data. What I did is identifying if embryos had (yes = 1) or not (no = 0) malformations or delay in their development, within samples of 15 embryos.
In one type of experiment, I repeated this 6 times (6 independent replicates); in another type, I repeated this 9 times (9 independent replicates), so my n is quite low.
I observed a lot of features (between 6 and 12) in every embryo, every 24 hours for 5 days (24 hpf, 48 hpf, 72 hpf and 96 hpf) so I have a lot of data.
My first idea was to do a "repeated measures ANOVA", but unfortunately, my data are absolutely NOT normal distributed; because they're "binary" counts (with minimal value 0 and maximum value 15), and I have lots of ties.
I tried to use Friedman's test, but it really takes too long! (At least 30 minutes for every feature).
I tried a lot of ways to normalize the data (even one I had never heard before: using the arcsin of the percentage square root...!), but nothing works.
Then a lab mate told me that I could calculate the confidence interval for my controls, and take as "different" from that any value that is outside the CI. I have the GraphPad Prism program, and it says that for calculating CI, you assume independent and gaussian distributed values. So I got stuck again at the beginning.
How can I calculate a non-parametric CI, using Prism?
On the other hand, I was thinking about using bootstrap, which theory I understand very basically, but I have never done it, and don't know if I can do it with this program.