Forward model uncertainties present a challenge for force field evaluation. In this work, we use a reduced chi-squared metric to evaluate the accuracy of a force field. This metric relies on several assumptions that in most practical cases do not hold. For one, it assumes the statistical independence of each measurement. More importantly, it also assumes that we have a priori estimates of the uncertainties σ j of each back-calculated observable j, a problematic requirement forward models such as chemical shift predictors. For example, SHIFTX2 reports uncertainties from cross-validation benchmarks, but the training and testing data of such models come from structures of well-folded globular proteins. Our use case here-heterogeneous collections of conformations sampled in molecular simulations-should necessitate a more forgiving value of the uncertainty. This is evidenced by the large reduced chi-squared values we computed for chemical shift observables. A similar scenario holds for the Karplus relation used as a forward model to calculate J-coupling constants. Yet, it is these forward model uncertainties that determine the relative importance of each observable when calculating the chi-squared value.