Resampling, repeatedly draws samples from the original observed data sample for the statistical inference of population parameters.
Boostrap, one of the most popular resampling methods, uses computer simulation for statistical inference without using an analytical formula such as a z-statistic or t-statistic.
Both the bootstrap and the Monte Carlo simulation build on repetitive sampling. Bootstrapping resamples a dataset as the true population, and infers from the sampling statistical distribution parameter values (i.e., mean, variance, skewness, and kurtosis) for the population. Monte Carlo simulation builds on generating random data with certain known statistical distribution of parameter values.
