Bootstrap methods are statistical techniques used to estimate the distribution of a sample statistic by resampling with replacement from the original data. This approach allows researchers to assess the variability and confidence intervals of estimates without relying on traditional parametric assumptions. These methods are particularly useful when the sample size is small or when the underlying distribution is unknown. By generating multiple simulated samples, bootstrap techniques provide a way to approximate the sampling distribution, enabling more robust statistical inference and hypothesis testing.