# Uncertainty analysis

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{{For|a more detailed treatment|Experimental uncertainty analysis}}
'''Uncertainty analysis''' investigates the uncertainty of variables that are used in [decision-making](/source/decision-making) problems in which observations and models represent the [knowledge base](/source/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 [experiment](/source/experiment)s '''uncertainty analysis''', or '''experimental uncertainty assessment''', deals with assessing the [uncertainty](/source/uncertainty) in a [measurement](/source/measurement). An experiment designed to determine an effect, demonstrate a law, or estimate the numerical value of a [physical variable](/source/physical_variable) will be affected by [error](/source/error)s due to instrumentation, methodology, presence of confounding effects and so on. Experimental uncertainty estimates are needed to assess the [confidence](/source/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](/source/design_of_experiments).

== Mathematical modelling ==
Likewise in numerical experiments and [modelling](/source/Mathematical_model) 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](/source/sensitivity_analysis).

== Calibrated parameters and output ==
A [calibrate](/source/calibrate)d [parameter](/source/parameter) does not necessarily represent [reality](/source/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](/source/Interval_finite_element)
* [Uncertainty quantification](/source/Uncertainty_quantification)
* [Propagation of uncertainty](/source/Propagation_of_uncertainty)
* [Measurement uncertainty#Uncertainty evaluation](/source/Measurement_uncertainty)

==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&ndash;1209.
*Santner, T. J.; Williams, B. J.; Notz, W.I. ''Design and Analysis of Computer Experiments''; Springer-Verlag, 2003.

Category:Measurement
Category:Decision-making

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