What is the purpose of sensitivity analyses in confirming robustness of results?

• A. To confirm the exactness of the baseline estimates.
• B. To show how results change under different assumptions or parameter values, indicating stability or uncertainty.
• C. To adjust results to always favor decision-makers.
• D. To eliminate uncertainty.

Answer: (B)

Sensitivity analyses explore how results respond when you vary key inputs and assumptions. In cost-related studies, you test changes to costs, effectiveness, time horizons, discount rates, or even the model structure. If the main conclusions stay the same across a wide range of plausible values, you gain confidence in the robustness of the findings; if results shift, you identify which inputs drive uncertainty and where more precise data would matter. This isn’t about proving exact baseline estimates or eliminating uncertainty or biasing results—it’s about understanding how sensitive the conclusions are to the choices made in the analysis.