All measurements are subject to error. When a reportable value is derived from a measurement or series of measurements, this value is only an estimate of the “true ...

Bayesian uncertainty analysis represents a powerful statistical framework that integrates prior knowledge with observed measurement data to quantify uncertainty in a consistent probabilistic manner.

Performing a measurement uncertainty calculation is often seen as problematic. One of the barriers to be overcome in the wider application of measurement uncertainty (MU) to reportable values is the ...

Monte Carlo methods have emerged as a crucial tool in the evaluation of measurement uncertainty, particularly for complex or non-linear measurement systems. By propagating full probability ...

GUM, the internationally approved technique for calculating measurement uncertainty, is reliant on the availability of a certified reference sample. Likewise, to find any repeatable offset (systematic ...

In the first round of testing, involving 60 measurements (six samples x five repeat tests x two rounds), an initial review of the results indicated that one sample consistently performed better than ...

Measurements are always made for a reason – to answer a particular question or to help solve a problem. Whenever a measurement is made there will always be some uncertainty about the result due to ...

In order to use the result of a chemical analysis for other purposes, its quality must be assessed. One important quality feature is the probability that the result coincides with the ‘true’ value. It ...

Measurement uncertainty, as described in ISO/IEC Guide 98, is a “parameter, associated with the result of a measurement, [which] characterizes the dispersion of values that could reasonably be ...

Ensuring parts meet specification is crucial and so it helps to have confidence in your measurement system. Parts that don’t meet specification, whether gauging composition or measuring thickness, ...