1. Introduction -- Traditional parametric statistical inference -- Bootstrap statistical inference -- Bootstrapping a regression model -- Theoretical justification -- The jackknife -- Monte Carlo evaluation of the bootstrap -- 2. Statistical inference using the bootstrap -- Bias estimation -- Bootstrap confidence intervals -- 3. Applications of bootstrap confidence intervals -- Confidence intervals for statistics with unknown sampling distributions -- The sample mean from a small sample -- The difference between two sample medians -- Inference when traditional distributional assumptions are violated -- OLS regression with a nonnormal error structure -- 4. Conclusion -- Future work -- Limitation of the bootstrap -- Concluding remarks