Limitations of and Alternatives to Traditional Uncertainty Quantification for Measurements

Measurement uncertainty of quantities that are functions of more than one measured variable are usually determined using propagation methods; either the Taylor Series Method or the Monte Carlo Method. Each of these requires critical assumptions that are more sweeping and potentially dangerous than commonly realized. The implications of these assumptions is explained along with some examples of how violation of these assumptions invalidates the uncertainty estimate. A new perspective based on consideration of potential error sources is presented ranging from the physical system being measured, the measurement system, the data reduction system, to the final experimental measurement result. An alternative approach that relies on sampling of error sources, whether known or unknown, is demonstrated through several examples. These examples demonstrate that the alternative approach can provide a more realistic estimate of total experimental uncertainty; one which is commonly much larger than that provided by traditional propagation methods.