To date, utility analysis research has derived point estimates of the expected utility value for human resource management programs or interventions. Utility estimates are usually quite large, but they fail to reflect the size and shape of the utility distribution. The present study investigated utility estimate variability for the selection utility of using the Programmer Aptitude Test to select computer programmers in a medium‐sized computer manufacturing organization. Utility calculations incorporated financial/economic factors as well as employee flows over time. The distributions for each utility parameter were empirically estimated, and these distribution estimates were combined through a Monte Carlo analysis to yield a distribution of total utility values. Monte Carlo results were compared to three other risk assessment approaches: (1) sensitivity analysis, (2) break‐even analysis, and (3) algebraic derivation of the distribution. Results suggest that the distribution information provided by the Monte Carlo analysis more completely described the variability and riskiness associated with the expected utility value. Future research suggested by these findings is discus