Once we have a data sample, it can be used to estimate the population parameter.
The problem is that we only have a single estimate of the population parameter, with little idea of the variability or uncertainty in the estimate.
One way to address this is by estimating the population parameter multiple times from our data sample. This is called resampling.
Statistical resampling methods are procedures that describe how to economically use available data to estimate a population parameter. The result can be both a more accurate estimate of the parameter (such as taking the mean of the estimates) and a quantification of the uncertainty of the estimate (such as adding a confidence interval).
Resampling methods are very easy to use, requiring little mathematical knowledge. They are methods that are easy to understand and implement compared to specialized statistical methods that may require deep technical skill in order to select and interpret.
The resampling methods […] are easy to learn and easy to apply. They require no mathematics beyond introductory high-school algebra, et are applicable in an exceptionally broad range of subject areas.