Today we looked at four different resampling techniques for estimation of parameter error. These techniques have the advantage of not requiring error propagation equations, but they do have their own issues. The Jackknife is a technique where you systematically drop a data point and recompute the fitting parameters describedhere. The Bootstrap is a technique where you randomly draw samples from your dataset and recompute the fitting parameters describedhere. Undersampling is a technique where you break your dataset into subsamples and recompute the fitting parameters for each as describedhere. Finally, Monte Carlo Simulation is a technique where you add random values characterized by known (or estimated) error and recompute the fitting parameters as describedhere.