I've just begun learning about the bootstrap method and I'm not entirely sure that I've understood this perfectly right. The way that I have interpreted what my book says is that when we don't have enough data or can't recreate/replicate exact data which we have had previously we simply randomise new data based on the data that we already have and then calculate whatever it is that we need to calculate with it.
I don't really understand why this method works or even is preferred as a good method, I mean... why should you expect anything else than results that are similar to the old test when you're uniformly "reassembling" your data from previously known data?
This aside, I've constructed a function in R which does the bootstrapping of a matrix filled with certain data, my problem is now to create a confidence interval for it. My book only says "calculate the 95% quantiles of the bootstrap-mean" but does not give me any formula on how to do so.