# When using bootstrap analysis, which estimates should be reported, the original ones or the bootstrap derived ones

I'm moving more and more to bootstrap for my analyses to estimate the variability of parameters of glm regression models.

I usually report the bootstrap estimate of the parameter (the mean of the bootstrap distribution), but from the theory I know that the bootstrap estimate should approach the original estimate if all $n^n$ possible resamples are evaluated to build the distribution.

So I started to wonder that maybe in the results I should report the original parameter instead of the bootstrap one, together with the bootstrap derived variability statistics (eg, BCa CIs)? Or maybe the bias corrected estimate ($2*\theta-\theta^*$)?

What is the common place and the right thing to do from your experience?