Efron's Bootstrap

Efron's Bootstrap is a statistical method used to estimate the uncertainty of a sample estimate, like a mean or proportion. Instead of just relying on the original data, it creates many new samples by randomly selecting observations from the original dataset. Each new sample is created with replacement, meaning the same observation can appear multiple times. By calculating the estimate for each of these new samples, you can see how much it varies, allowing you to make more informed statements about the reliability of your original estimate. This technique helps in understanding potential variability in real-world data analysis.