Strength and Weakness of Bootstrap Sampling

By Kardi Teknomo, PhD.

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• (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.

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Preferable reference for this tutorial is

Teknomo, Kardi. Bootstrap Sampling Tutorial. http://people.revoledu.com/kard/ tutorial/bootstrap/