Bootstrapping
Bootstrapping is a method of resampling that uses random sampling – typically with replacement – to generate statistical estimates about a population by resampling from subsets of the sampled distribution, such as the following:
- Confidence intervals
- Standard error
- Correlation coefficients (Pearson’s correlation)
The idea is that repeatedly sampling different random subsets of a sample distribution and taking the average each time, given enough repeats, will begin to approximate the true population using each subsample’s average. This follows directly the concept of the Central Limit Theorem, which to be restated, asserts that sampling means begins to approximate normal sampling distributions, centered around the original distribution’s mean, as sample sizes and counts increase. Bootstrapping is useful when a limited quantity of samples exists in a distribution relative to the amount needed for a specific test,...