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.
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. [1] A related field is the design of experiments.
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.
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. [2]