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Validation and the computational efficiency of an analytical alternative to Monte Carlo simulation for quantifying risks in project performance measures such as time, cost, net present value and internal rate of return are explored in this paper. The analytical approach is based on the use of the Pearson family of distributions, a four moment characterization of uncertainty for input and output variables and a modified version of the PNET algorithm for modelling time uncertainty. The approach is applied to a generalized hierarchical description of a project's economic structure. Results show that the analytical approach can duplicate results of a full-scale Monte Carlo simulation with approximately 0.033 of the computational effort.