meters, and variables aimed to characterise the process being investigated. Input is subject to many sources of uncertainty including errors of measurement, absence of information and poor or partial understanding of the driving forces and mechanisms. This imposes a limit on our confidence in the response or output of the model. Further, models may have to cope with the natural intrinsic variability of the system, such as the occurrence of stochastic events. Good modelling practice requires that the modeller provides an evaluation of the confidence in the model, possibly assessing the uncertainties associated with the modelling process and with the outcome of the model itself. Uncertainty and sensitivity analysis offer valid tools for characterising the uncertainty associated with a model.
More information: Sensitivity analysis