Sensitivity analysis examines how changes in model parameters affect the outcomes, helping to identify which parameters have the most significant impact on model predictions. Uncertainty analysis quantifies the uncertainty in model predictions due to variability in the input data and parameters. These analyses are vital for understanding the robustness of the model and for making informed decisions under uncertainty.
