Bootstrap Methods in Statistics

Bootstrap methods in statistics are techniques used to estimate the properties of a population by resampling from a sample dataset. Essentially, you take your original data and repeatedly create "new" datasets by randomly selecting observations with replacement. This allows you to assess the variability and confidence of statistical estimates, such as means or medians. By analyzing these resampled datasets, you can gain insights into the reliability of your findings, making bootstrap methods valuable for estimating uncertainties in research without requiring strong assumptions about the underlying population.