Summary: We develop and test a strategy for selectively sizing (multiplying by an appropriate scalar) the approximate Hessian matrix before it is updated in the BFGS and DFP trust-region methods for unconstrained optimization. Our numerical results imply that, for use with the DFP update, the Oren- Luenberger sizing factor is completely satisfactory and selective sizing is vastly superior to the alternatives of never sizing or first-iteration sizing and is slightly better than the alternative of always sizing. Numerical experimentation showed that the Oren-Luenberger sizing factor is not a satisfactory sizing factor for use with the BFGS update. Therefore, based on our newly acquired understanding of the situation, we propose a centered Oren-Luenberger sizing factor to be used with the BFGS update. Our numerical experimentation implies that selectively sizing the BFGS update with the centered Oren-Luenberger sizing factor is superior to the alternatives. These results contradict the folk axiom that sizing should be done only at the first iteration. They also show that, without sufficient sizing, DFP is vastly inferior to BFGS; however, when selectively sized, DFP is competitive with BFGS.