BCA (Bias-Corrected and Accelerated) method

The Bias-Corrected and Accelerated (BCa) method is a statistical technique used to improve the accuracy of confidence intervals derived from bootstrap samples. Bootstrapping involves repeatedly resampling data to estimate variability, but it can sometimes produce misleading intervals. BCa adjusts these intervals to account for any bias and skewness in the data distribution, leading to more reliable estimates. It does this by calculating correction factors for bias and acceleration (curvature), providing a confidence interval that better reflects the true uncertainty, especially in small or skewed samples.