A Monte Carlo method is presented to separate and study the effects of systematic and random errors present in thermodynamic data on chemical process design and simulation. From analysis of thermodynamic data found in the literature, there is clear evidence of the presence of systematic errors, in particular, for liquid−liquid equilibria data. For systematic errors, the data are perturbed systematically with a rectangular probability distribution, and to analyze random errors, the perturbation is carried out randomly with normal probability distributions. Thermodynamic parameters are obtained from appropriate regression methods and used to simulate a given unit operation and to obtain cumulative frequency distributions, providing a quantitative risk assessment and a better understanding of the role of uncertainty in process design and simulation. The results show that the proposed method can clearly distinguish when one type of error is more significant. Potential applications are safety factors, process modeling, and experimental design.