摘要：AbstractThe most popular SA practice seen in the literature is that of ‘one-factor-at-a-time’ (OAT). This consists of analyzing the effect of varying one model input factor at a time while keeping all other fixed. While the shortcomings of OAT are known from the statistical literature, its widespread use among modellers raises concern on the quality of the associated sensitivity analyses. We introduce a novel geometric proof of the inefficiency of OAT, with the purpose of providing the modelling community with a convincing and possibly definitive argument against OAT. Alternatives to OAT are indicated which are based on statistical theory, drawing from experimental design, regression analysis and sensitivity analysis proper.
