Jackknife resampling

Jackknife resampling is a technique used to estimate the variability or uncertainty of a statistical estimate, like an average or a trend. It involves systematically leaving out one data point at a time from your dataset, recalculating the estimate each time. By doing this for every data point, you get multiple versions of the estimate. Analyzing these versions helps you understand how sensitive your overall result is to individual data points and provides an estimate of the measure’s reliability. This method is simple, computationally efficient, and widely used in statistical analysis to assess the stability of your findings.