Permutation tests are significance tests based on permutation resamples drawn at random from the original data. Permutation resamples are drawn without replacement, in contrast to bootstrap samples, which are drawn with replacement. Here is an example I did in R of a simple permutation test. (Your comments are welcome)
Permutation tests have great advantages. They do not require specific population shapes such as normality. They apply to a variety of statistics, not just to statistics that have a simple distribution under the null hypothesis. They can give very accurate p-values, regardless of the shape and size of the population (if enough permutations are used).
I have also read that it is often useful to give a confidence interval along with a test, which is created using bootstrap resampling rather than permutation resampling.
Could you explain (or just give the R code) how a confidence interval is constructed (i.e. for the difference between the means of the two samples in the above example) ?
After some googling I found this interesting reading.