Bootstrapping
Given a set of \( N \) data, assume that we are interested in some
observable \( \theta \) which may be estimated from that set. This observable can also be for example the result of a fit based on all \( N \) raw data.
Let us call the value of the observable obtained from the original
data set \( \hat{\theta} \). One recreates from the sample repeatedly
other samples by choosing randomly \( N \) data out of the original set.
This costs essentially nothing, since we just recycle the original data set for the building of new sets.