Why so many published sensitivity analyses are false: a systematic review of sensitivity analysis practices

Abstract

Sensitivity analysis provides information on the relative importance of model input parameters and assumptions. It is distinct from uncertainty analysis, which addresses the question 'How uncertain is the prediction?' Uncertainty analysis needs to map what a model does when selected input assumptions and parameters are left free to vary over their range of existence, and this is equally true of a sensitivity analysis. Despite this, many uncertainty and sensitivity analyses still explore the input space moving along one-dimensional corridors leaving space of the input factors mostly unexplored. Our extensive systematic literature review shows that many highly cited papers (42% in the present analysis) fail the elementary requirement to properly explore the space of the input factors. The results, while discipline-dependent, point to a worrying lack of standards and recognized good practices. We end by exploring possible reasons for this problem, and suggest some guidelines for proper use of the methods.