This practical work studies Jackknife and Bootstrap resampling methods through Monte Carlo simulations. It compares their performance for bias and variance estimation, confidence intervals, survival probabilities, sample medians, correlations, and regression coefficients under different distributions. The study shows that bootstrap methods are generally more robust, while jackknife is useful for smooth statistics and bias correction.