Bootstrap Theory

Bootstrap Theory is a statistical method that involves repeatedly resampling a dataset with replacement to estimate the variability or uncertainty of a statistic, like a mean or median. By creating many simulated samples, it helps determine how much a statistic might vary if we collected new data, without needing additional data collection. This approach provides more reliable confidence intervals and insights about the stability of our results, making it a powerful tool for assessing the reliability of findings in research and data analysis.