How does sample size "work" for Bayesian statistics?
I've just started reading into Bayesian statistics and I'm pretty smitten by it even though I haven't fully understood all of it yet.
I'm a PhD student and I'm considering using Bayesian statistics for my sample. I'm looking at humans in Isolated and Confined Environments (ICE), which means that by default the data is extremely difficult to obtain and the sample size is tiny. The issue is that the total possible population is exactly 11 people, of whom 1 has withdrawn from the study and 1 may need to be excluded from the data. The person who may need to be excluded has developed major psychiatric problems and has been evacuated. Even though I will be able to collect more data from them, this will be from their home country/town and not from within the ICE, so it makes no sense to include that data in the the ICE analysis.
I do have an age & gender-matched control group.
So, would Bayesian statistics even be appropriate? I've found a paper (Stiger et al., 1998) that suggests you can use normal ANOVA with small samples and ordinal data if you use a Huyn-Feldt correction, but I can't seem to find a direct answer for Bayesian statistics...
Thank you in advance!
eniseg the newbie