What is resampling?

Resampling is a statistical method used in data analysis to estimate the precision of a sample statistic by repeatedly drawing samples from a data set and calculating the statistic of interest.

There are two main types of resampling techniques:

1. Bootstrapping: Bootstrapping involves sampling with replacement from the original data set to create multiple "bootstrap" samples. The statistic of interest (such as the mean or median) is then calculated for each bootstrap sample, and a distribution of these statistics is generated to estimate the sampling variability.

2. Jackknifing: Jackknifing involves systematically leaving out one observation at a time from the data set to create multiple subsets. The statistic of interest is then calculated for each subset, and the results are used to estimate the bias and variance of the statistic.

Resampling methods are particularly useful when the underlying distribution of the data is unknown or when the sample size is small. They can provide more reliable estimates of uncertainty and help to identify outliers and influential data points. Resampling techniques are commonly used in fields such as statistics, machine learning, and data science.