In statistics,<unk> is a modern, computer-intensive, general purpose approach to statistical inference, falling within a broader class of resampling methods.<unk> is the practice of estimating properties of an estimator (such as its variance) by measuring those properties when sampling from an approximating distribution. One standard choice for an approximating distribution is the empirical distribution of the observed data.
It may also be used for constructing hypothesis tests. It is often used as an alternative to inference based on parametric assumptions when those assumptions are in doubt, or where parametric inference is impossible or requires very complicated formulas for the calculation of standard errors.
The advantage of<unk> over analytical method is its great simplicity - 
it is straightforward to apply the bootstrap to derive estimates of standard errors and confidence intervals for complex estimators of complex parameters of the distribution, such as percentile points, proportions, odds ratio, and correlation coefficients.
The disadvantage of<unk> is that while (under some conditions) it is asymptotically consistent, it does not provide general finite sample guarantees, and has a tendency to be overly optimistic.[citation needed]
Bootstrapping 
Linear regression
Non-parametric statistics
Fixed effects estimation
Bayesian inference
Receiver operating characteristic
Ancillary statistic
Analysis of covariance