The jackknife is a general non-parametric method for estimation of the bias and variance of a statistic (which is usually an estimator) using only the sample itself. The jackknife is considered as the predecessor of the bootstrapping techniques.
With a sample of size N, the jackknife involves calculating N values of the estimator, with each value calculated on the basis of the entire sample less one observation. The first value of the estimator is calulated without using the first sample observation, the second value of the estimator is calculated without using the second sample observation, and so on. Then, jackknife estimates of the bias and variance are calculated from simple formulas on the basis of the N calculated values of the estimator.