Return values can be estimated from large data sets stemming from numerical models. The question explored here is how much of the original data must be kept in order to compute unbiased return estimates. We find that retaining only a small fraction is usually enough. This offers huge storage and computational savings. We provide a set of examples to demonstrate how this can be done.