Strength and Weakness of Bootstrap Sampling
- (Negative) Bootstrap method is not exact. For large sample, permutation test perform better than bootstrap.
- (Positive) Bootstrap requires very minimum assumption. Even if permutation test fail, bootstrap method still can do.
- (Positive) Though it can be used for parametric method (i.e. distribution is known), bootstrap method is most useful when the sample distribution is unknown (non-parametric).
Note: Permutation test is similar to bootstrap that it resample from the sample but not randomly. Instead, it considers all possible permutation of the sample.
Preferable reference for this tutorial is
Teknomo, Kardi. Bootstrap Sampling Tutorial. http://people.revoledu.com/kardi/tutorial/Bootstrap/