Which factors are essential in conducting sensitivity analysis?

Sensitivity analysis is a technique used to determine how the different values of an independent variable impact a particular dependent variable under a given set of assumptions. The primary focus is on understanding the relationship between input parameters and outputs in a model, which is crucial for decision-making and risk assessment.

In this context, input variability refers to the fluctuations or changes in the input values that are being analyzed. This variability is essential as it allows the analyst to see how changes in inputs can affect outcomes. Parameter impacts are also vital because they help to identify which parameters have the most significant influence on the results, aiding in prioritization and focus during analysis. Lastly, uncertainties in models highlight the potential errors or variations in the modeling process itself, which is important for assessing the reliability of the conclusions drawn from the analysis.

By examining these three factors—input variability, parameter impacts, and uncertainties—analysts can gain valuable insights into the robustness of their models and the sensitivity of their results to changes in input values. This leads to more informed decision-making in design, resource allocation, and risk management.