Abstract
Sensitivity analysis reveals the relative weights of the assumptions and input parameters used in the model. It differs from uncertainty analysis, which deals with the issue of how uncertain the forecast is. Both sensitivity and uncertainty analyses must map on a model behaves when certain input assumptions and parameters are allowed to fluctuate within the range of possible values. While going down one-dimensional corridors, various uncertainties and sensitivity studies continue investigating the input space, leaving room for the most undiscovered input elements. Numerous highly cited publications fall short of the fundamental criteria to thoroughly investigate the space of the input components, according to a thorough systematic examination of the literature. Despite being discipline-specific, the findings show a concerning absence of good practices and accepted norms. The conclusion listed a few potential causes for this issue and offered suggestions for how the approaches should be applied correctly.
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