![]() ![]() The results are fully backed by data, there is no room for assumptions which are not supported by evidence. Proponents of MSA would stress that this is a good thing. All of the results come from experimental data. A fundamental difference in MSA is that there are no Type B estimates. Uncertainty evaluation may include both experimental variation data (Type A estimates) and other estimates of uncertainties (Type B estimates). In the previous articles I focused on uncertainty evaluation methods. If you’re not familiar with key principles they you’d be best to start at the beginning of this series. The final part is Part 5: Uncertainty Evaluation using MSA Tools. The previous pages included Part 1: Key Principles in Metrology and Measurement Systems Analysis (MSA), Part 2: Uncertainty of Measurement and Part 3: Uncertainty Budgets. This page is the fourth part in a series of pages explaining the science of good measurement. In MSA accuracy is considered to be the combination of trueness (bias) and precision (variation). Analysis of the measurement results may allow individual components of variation to be quantified. Repeated measurements are used to determine variation and bias. ![]() They are the standard way of doing this in manufacturing. Measurement Systems Analysis (MSA) and in particular Gage R&R studies are tests used to determine the accuracy of measurements.
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