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

@article{Saltelli2019WhySM, title={Why so many published sensitivity analyses are false: A systematic review of sensitivity analysis practices}, author={A. Saltelli and Ksenia A. Kalinina and W. Becker and P. Fennell and F. Ferretti and N. Holst and Sushan Li and Qiongli Wu}, journal={Environ. Model. Softw.}, year={2019}, volume={114}, pages={29-39} }

Sensitivity analysis (SA) has much to offer for a very large class of applications, such as model selection, calibration, optimization, quality assurance and many others. Sensitivity analysis offers crucial contextual information regarding a prediction by answering the question "Which uncertain input factors are responsible for the uncertainty in the prediction?" SA is distinct from uncertainty analysis (UA), which instead addresses the question "How uncertain is the prediction?" As we discuss… CONTINUE READING

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