{{For|a more detailed treatment|Experimental uncertainty analysis}} '''Uncertainty analysis''' investigates the uncertainty of variables that are used in decision-making problems in which observations and models represent the knowledge base. In other words, uncertainty analysis aims to make a technical contribution to decision-making through the quantification of uncertainties in the relevant variables.
== Physical experiments == In physical experiments '''uncertainty analysis''', or '''experimental uncertainty assessment''', deals with assessing the uncertainty in a measurement. An experiment designed to determine an effect, demonstrate a law, or estimate the numerical value of a physical variable will be affected by errors due to instrumentation, methodology, presence of confounding effects and so on. Experimental uncertainty estimates are needed to assess the confidence in the results.<ref>{{cite web|url=http://www.engineering.uiowa.edu/~cfd/pdfs/References/uncert.pdf|title=Summary of experimental uncertainty assessment methodology with example|publisher=|access-date=2008-05-23|archive-date=2008-12-30|archive-url=https://web.archive.org/web/20081230153958/http://www.engineering.uiowa.edu/~cfd/pdfs/References/uncert.pdf|url-status=dead}}</ref> A related field is the design of experiments.
== Mathematical modelling == Likewise in numerical experiments and modelling uncertainty analysis draws upon a number of techniques for determining the reliability of model predictions, accounting for various sources of uncertainty in model input and design. A related field is sensitivity analysis.
== Calibrated parameters and output == A calibrated parameter does not necessarily represent reality, as reality is much more complex. Any prediction has its complexities of reality that cannot be represented uniquely in the calibrated model; therefore, there is a potential error. Such errors must be accounted for when making management decisions on the basis of model outcomes. <ref>{{cite web|url=http://www.pesthomepage.org/Uncertainty_Analysis.php|title=PEST Uncertainty Analysis|website=www.pesthomepage.org|access-date=2011-11-07|archive-date=2020-07-26|archive-url=https://web.archive.org/web/20200726230410/http://www.pesthomepage.org/Uncertainty_Analysis.php|url-status=dead}}</ref>
==See also== * Interval finite element * Uncertainty quantification * Propagation of uncertainty * Measurement uncertainty#Uncertainty evaluation
==References== {{Reflist}}
==Bibliography==
*Etienne de Rocquigny, Nicolas, Devictor, Stefano, Tarantola (Editors), ''Uncertainty in Industrial Practice: A Guide to Quantitative Uncertainty Management'', Wiley & Sons Publishers, 2008. *J.C. Helton, J.D. Johnson, C.J. Salaberry, and C.B. Storlie, 2006, Survey of sampling based methods for uncertainty and sensitivity analysis. ''Reliability Engineering and System Safety'', '''91''':1175–1209. *Santner, T. J.; Williams, B. J.; Notz, W.I. ''Design and Analysis of Computer Experiments''; Springer-Verlag, 2003.
Category:Measurement Category:Decision-making