What is Sensitivity Analysis?
Sensitivity analysis is a tool used to evaluate how the uncertainty in the output of a model or system can be attributed to different sources of uncertainty in the input. It is a way of understanding how sensitive the output of a model is to changes in the input, and can help identify which input variables are the most important in driving the model's output.
There are several types of sensitivity analysis that can be performed, including:
- One-factor-at-a-time (OFAT) sensitivity analysis: This type of analysis involves changing one input variable at a time and observing the effect on the output.
- Scenario analysis: In scenario analysis, different combinations of input variables are changed to see how they impact the output.
- Monte Carlo simulation: This type of sensitivity analysis involves generating multiple random samples of the input variables and analyzing the resulting outputs to understand the distribution of outcomes.
Sensitivity analysis can be useful in a variety of contexts, such as financial modeling, risk analysis, and decision-making. It can help identify key drivers of uncertainty and inform decision-makers about the potential risks and opportunities associated with different courses of action.