  This study compares the performances of two sampling-based strategies for the
simultaneous estimation of the first-and total-orders variance-based
sensitivity indices (a.k.a Sobol' indices). The first strategy was introduced
by [8] and is the current approach employed by practitioners. The second one
was only recently introduced by the authors of the present article. They both
rely on different estimators of first-and total-orders Sobol' indices. The
asymp-totic normal variances of the two sets of estimators are established and
their accuracies are compared theoretically and numerically. The results show
that the new strategy outperforms the current one.Keywords: global sensitivity
analysis, variance-based sensitivity indices, first-order Sobol' index,
total-order Sobol' index, Monte Carlo estimate, asymptotic normality
