  In the finite-size scaling analysis of Monte Carlo data, instead of computing
the observables at fixed Hamiltonian parameters, one may choose to keep a
renormalization-group invariant quantity, also called phenomenological
coupling, fixed at a given value. Within this scheme of finite-size scaling, we
exploit the statistical covariance between the observables in a Monte Carlo
simulation in order to reduce the statistical errors of the quantities involved
in the computation of the critical exponents. This method is general and does
not require additional computational time. This approach is demonstrated in the
Ising model in two and three dimensions, where large gain factors in CPU time
are obtained.
