We explore the tasks where sensitivity analysis (SA) can be useful and try to assess the relevance of SA within the modeling process. We suggest that SA could considerably assist in the use of models, by providing objective criteria of judgement for different phases of the model­building process: model identification and discrimination; model calibration; model corroboration.

We review some new global quantitative SA methods and suggest that these might enlarge the scope for sensitivity analysis in computational and statistical modeling practice. Among the advantages of the new methods are their robustness, model independence and computational convenience.

The discussion is based on worked examples.