Which statement best describes deterministic vs probabilistic sensitivity analysis?

• A. Deterministic varies one or few parameters at a time; probabilistic assigns distributions and runs simulations
• B. Deterministic uses distributions; probabilistic uses single-point estimates
• C. Deterministic ignores uncertainty; probabilistic accounts for all
• D. Deterministic is only for costs; probabilistic only for effects

Answer: (B)

Deterministic sensitivity analysis tests how results change when you vary one parameter at a time while keeping the others fixed, giving a clear view of which inputs have the biggest influence on the outcome. Probabilistic sensitivity analysis, in contrast, assigns probability distributions to many uncertain parameters and runs many simulations (often via Monte Carlo) to propagate that uncertainty through the model. The outcome is a distribution of results that reflects overall uncertainty rather than a single point estimate.

In practice, this means deterministic analysis helps identify influential inputs, while probabilistic analysis provides a sense of the range and likelihood of possible results. The statements that describe using distributions with deterministic analysis or using single-point estimates in probabilistic analysis don’t align with how these methods are typically applied, so they don’t capture the real distinction between the two approaches.