a procedure in which samples are randomly drawn, with replacement, from an initial data set and their parameters and standard errors estimated and averaged across the set of samples. This is then followed by another random sampling of the data, again with replacement, after which parameter estimates and standard errors are obtained a second time. Double bootstrapping usually is less biased than using a single set of bootstrapping samples.