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 strict assumptions about the underlying population distribution. These methods are particularly useful in situations where traditional parametric methods may not be appropriate. By generating multiple simulated samples, bootstrap methods can provide insights into the stability and reliability of estimates, making them valuable tools in fields such as statistics, machine learning, and data analysis.