  A promising method for calculating free energy differences Delta F is to
generate non-equilibrium data via ``fast-growth'' simulations or experiments --
and then use Jarzynski's equality. However, a difficulty with using Jarzynski's
equality is that Delta F estimates converge very slowly and unreliably due to
the nonlinear nature of the calculation -- thus requiring large, costly data
sets. Here, we present new analyses of non-equilibrium data from various
simulated molecular systems exploiting statistical properties of Jarzynski's
equality. Using a fully automated procedure, with no user-input parameters, our
results suggest that good estimates of Delta F can be obtained using 6-15 fold
less data than was previously possible. Systematizing and extending previous
work [1], the new results exploit the systematic behavior of bias due to finite
sample size. A key innovation is better use of the more statistically reliable
information available from the raw data.
