Sensitivity analysis is the study of how the uncertainty in the output of a mathematical model or system (numerical or otherwise) can be apportioned to different sources of uncertainty in its inputs. A related practice is uncertainty analysis, which has a greater focus on uncertainty quantification and propagation of uncertainty. Ideally, uncertainty and sensitivity analysis should be run in tandem.
Sensitivity analysis can be useful for a range of purposes, including:
* Testing the robust decision|robustness of the results of a model or system in the presence of uncertainty.
* Increased understanding of the relationships between input and output variables in a system or model.
* Uncertainty reduction: identifying model inputs that cause significant uncertainty in the output and should therefore be the focus of attention if the robustness is to be increased (perhaps by further research).
* Searching for errors in the model (by encountering unexpected relationships between inputs and outputs).
* Model simplification – fixing model inputs that have no effect on the output, or identifying and removing redundant parts of the model structure.
* Enhancing communication from modelers to decision makers (e.g. by making recommendations more credible, understandable, compelling or persuasive).
* Finding regions in the space of input factors for which the model output is either maximum or minimum or meets some optimum criterion (see optimization and Monte Carlo filtering).
Taking an example from economics, in any budgeting process there are always variables that are uncertain. Future tax rates, interest rates, inflation rates, headcount, operating expenses and other variables may not be known with great precision. Sensitivity analysis answers the question, if these variables deviate from expectations, what will the effect be (on the business, model, system, or whatever is being analyzed), and which variables are causing it?

Sensitivity analysis is the study of how the uncertainty in the output of a mathematical model or system (numerical or otherwise) can be apportioned to different sources of uncertainty in its inputs. A related practice is uncertainty analysis, which has a greater focus on uncertainty quantification and propagation of uncertainty. Ideally, uncertainty and sensitivity analysis should be run in tandem.
Sensitivity analysis can be useful for a range of purposes, including:
* Testing the robust decision|robustness of the results of a model or system in the presence of uncertainty.
* Increased understanding of the relationships between input and output variables in a system or model.
* Uncertainty reduction: identifying model inputs that cause significant uncertainty in the output and should therefore be the focus of attention if the robustness is to be increased (perhaps by further research).
* Searching for errors in the model (by encountering unexpected relationships between inputs and...