Model updating is a very active research field, in which significant effort has been invested in recent years. Model updating methodologies are invariably successful when used on noise-free simulated data, but tend to be unpredictable when presented with real experimental data that are-unavoidably-corrupted with uncorrelated noise content. In the development and validation of model-updating strategies, a random zero-mean Gaussian variable is added to simulated test data to tax the updating routines more fully. This paper proposes a more sophisticated model for experimental measurement noise, and this is used in conjunction with several different frequency response function estimators, from the classical H and H to more refined estimators that purport to be unbiased. Finite-element model case studies, in conjunction with a genuine experimental test, suggest that the proposed noise model is a more realistic representation of experimental noise phenomena. The choice of estimator is shown to have a significant influence on the viability of the FRF sensitivity method. These test cases find that the use of the H estimator for model updating purposes is contraindicated, and that there is no advantage to be gained by using the sophisticated estimators over the classical H estimator.