 Sensitivity index

Under an equal-variance Gaussian evidence model, $d^{\prime }$ is separation of signal and noise means in units of their common standard deviation. A yes/no task gives $d^{\prime }={\Phi }^{-1}(H)-{\Phi }^{-1}(F)$, where $H$ and $F$ are hit and false-alarm probabilities. This formula assumes the model; it does not define every possible sensory-discrimination measure.

The Sensitivity Index is a measure used to quantify how sensitive a particular outcome is to changes in input variables. It is commonly employed in various fields such as finance, risk management, environmental studies, and epidemiology, among others. The concept helps analysts understand the impact of uncertainty in input variables on the final results of a model or system.