Thermodynamic models and experimental data exhibit the usual systematic and random errors. The severity of their errors depends on their use, such as for process calculations in a process simulator. Similarly, the value of better thermodynamic models and/or data should be measured with reference to such use. We have developed techniques for quantification of such thermodynamic-induce process uncertainties via Monte Carlo simulation, regression analyses, and analogies to optimization. The influence of experimental data sources and data types on the uncertainty of thermodynamic models is studied. Details and applications of our new sampling strategy (EPS), which accounts for the high degree of correlation between thermodynamics model parameters, is given. This procedure directly uses the regression results in a way that is much more powerful and mathematically accurate than traditional covariance matrix techniques. Level sets are used for the Monte Carlo samples so that unbiased accurate sampling of the entire feasible region is obtained. Comparison with traditional Monte Carlo sampling, Latin Hypercube sampling (LHS), and Shifted Hammersley sampling (SHS) are shown. The result is an unbiased estimate of uncertainties that reduces the over- and under-estimations common in traditional techniques. The approaches presented can be used for safety-factor/risk analysis, guidelines for simulator use, experimental design, and model comparisons. They allow determinations of the value of obtaining additional phase-equilibrium data and the potential value of improved phase-equilibrium models. Examples and case studies of these applications are provided.