In other words, uncertainty analysis aims to make a technical contribution to decision-making through the quantification of uncertainties in the relevant variables.
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.
Any prediction has its own 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.