Overview
Bootstrapping allows researchers to estimate the properties of an estimator (such as its variance) by measuring those properties when sampling from an approximating distribution. It is particularly useful when the underlying distribution is unknown or when the sample size is small.
The Process
- Take a sample of size N from the original dataset.
- Repeatedly draw samples of size N from this sample with replacement.
- Calculate the statistic of interest (e.g., mean) for each bootstrap sample.
- Analyze the distribution of these statistics.
Use Cases
- Estimating confidence intervals.
- Validating machine learning models (e.g., in Random Forests).