An exponential and heavy tail analysis method is presented to study the effect of systematic and random errors present in thermodynamic data on chemical process design and simulation. The true distribution tail characteristics (important for high levels of quality assurance) can be far from the estimates obtained with typical Gaussian distribution analysis. Pareto (heavy) or exponential distributions may represent the tail behavior better under many circumstances. Heavy tails diminish at an algebraic rate rather than at an exponential rate. Different error types such as random and systematic error can potentially cause different effects in the behavior of probability distributions, particularly in the tails. In this work, we use the tail behavior of cumulative frequency distributions produced from uncertainty analyses to characterize the error propagation in process design and simulation. The diminishing rate of the tail of a given distribution can be related to the error types involved in the process and also can be used to determine which error exhibits stochastic dominance. Case studies of process performance evaluations for liquid-liquid extraction operations are presented to illustrate the approach. It is observed that random and systematic errors (coupled with typical nonlinear chemical engineering models) can cause the tails of the uncertainty probability distributions to be exponential.