I have a series of questions as I know of Monte Carlo Simulation but have not used them.

1. Is the basic gist: Get mean and sd from data (can be very small n = 5). Assume the data is a normal distribution and draw n random samples from that distribution. Take the average of these draws and use quantiles to calculate a confidence interval?

2. Does the sampling distribution have to be normal? So for example the outcome for agile stories is number of stories for a sprint. This tells me the distribution is likely Poisson or negative binomial. Should I instead try to draw from one of these distributions (though I don't think I can get all the parameters from a very small data set to plug in to the sampling)?

3. What if I wanted to include months as a variable? Would I instead run 12 MC simulations for each month and compute the forcast that way?

4. Given a small data set (~4 months or 8 sprints [8 observations]) and count outcome is there a better technique or other techniques that could be investigated?

5. It seems that MC is very similar to bootstrapping except you draw from an assumed distribution rather than resampling from empirical data. Is this true that the techniques are similar with the exception of what's being sample? If so it seems bootstrapping would need more data?