Which statement best differentiates scenario analysis from probabilistic sensitivity analysis?

• A. Scenario analysis examines predefined alternative circumstances; PSA quantifies parameter uncertainty across distributions.
• B. PSA examines predefined scenarios; scenario analysis quantifies distributions.
• C. PSA ignores uncertainty.
• D. Both analyze identical uncertainties.

Answer: (A)

Distinguishing how these two approaches handle uncertainty is the key idea. In scenario analysis, you pick a few predefined future circumstances and compare outcomes under each, treating those scenarios as fixed, distinct worlds. There isn’t a formal probability attached to each scenario; the focus is on understanding how results change when the environment or assumptions shift.

In probabilistic sensitivity analysis, uncertainty is embedded in the inputs themselves by describing them with probability distributions. You repeatedly sample from those distributions (often many thousands of times) to see how the results vary, producing a distribution of outcomes that reflects parameter uncertainty.

So the best statement captures the difference: scenario analysis uses predefined alternative circumstances, while PSA quantifies parameter uncertainty across distributions. The other options misstate how each method handles uncertainty—PSA does not rely on predefined scenarios, scenario analysis does not quantify distributions, and PSA does not ignore uncertainty.