Model updating is useful for improving structural performance assessments. This paper examines an important assumption of traditional model-updating approaches. This assumption requires the error independence between points where predictions and measurements are compared. Simulations performed on a full-scale bridge show that uncertainties are correlated for both static and dynamic predictions. Therefore, traditional model-updating techniques are not appropriate in such situations. Model updating limitations related to randomness and independence of uncertainties may be overcome by an interpretation strategy called Candidate Model Search for System Identification (CMS4SI). Instead of judging a model by its ability to fit measured data, the approach falsifies models using threshold values that are based upon uncertainties. Uncertainties may be correlated, systematic, independent or random.