[en] Since their initial development nearly 20 years ago, sensitivity/uncertainty (S/U) techniques have been increasingly applied to criticality safety validation. These techniques can generally be applied quickly and for a range of purposes after the initial investment of calculating sensitivities for a safety analysis model and a range of potential benchmark experiments. This paper discusses these applications and how they have been affected by changes in covariance data libraries. Critical experiment selection is the most prominent area in which S/U methods impact validation efforts. These methods allow for rigorous calculation of similarity between safety analysis models and potential benchmarks. The most widely used of these methods is the integral parameter c_k, although other integral parameters such as E and some non-integral parameters have also been explored. The c_k parameter calculates the fraction of nuclear data-induced uncertainty shared between an application and an experiment; this metric is predicated on the assessment that nuclear data errors are the most likely source of computational bias. Therefore, values of c_k are inherently dependent on the nuclear covariance library used to calculate them and inevitably change when these data change. Past studies have shown how much the c_k values can vary, especially in difficult validation cases like burnup credit. These significant changes are the result of differences in the relative uncertainties of nuclides with large sensitivities, and they imply differences in benchmark applicability to the safety analysis model. This paper demonstrates that more common criticality safety models involving single fissile species have significantly less variability of c_k between covariance libraries, thus enhancing confidence in the application of S/U methods for experiment selection in these scenarios. This paper presents case studies examining the impact of these changes on some hypothetical safety analysis systems. (authors)