Stochastic sensitivity analysis explicitly considers the uncertainty common in engineering economy. Like simulation, the variables probability distributions are the input. The output, like deterministic sensitivity analyses, relates changing variables to changes in the model's outcome (present worth). However, the x —axis is now probabilistic so that break-even probabilities are r-axis distances. Areas represent conditional expected values and expected values of perfect information. This solid intuitive basis contrasts with a uniform pdf assumption that underlies typical sensitivity graphs. The probability metric permits relative sensitivity analysis of variables that are measured in different units. Spreadsheets easily support this method.