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2.
Measurement Process Characterization
2.5. Uncertainty analysis
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| Type B evaluations apply to both error and bias | Type B evaluations can apply to both random error and bias. The distinguishing feature is that the calculation of the uncertainty component is not based on a statistical analysis of data. The distinction to keep in mind with regard to random error and bias is that:
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| Sources of type B evaluations | Some examples of sources of uncertainty that lead to type B evaluations are:
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| Documented sources of uncertainty from other processes | Documented sources of uncertainty, such as calibration reports for reference standards or published reports of uncertainties for physical constants, pose no difficulties in the analysis. The uncertainty will usually be reported as an expanded uncertainty, U, which is converted to the standard uncertainty,
If the k factor is not known or documented, it is probably conservative to assume that k = 2. | ||
| Sources of uncertainty that are local to the measurement process | Sources of uncertainty which are local to
the measurement process but which cannot be adequately sampled
to allow a statistical analysis require type B evaluations.
One technique, which is widely used, is to estimate the worst-case effect,
a, for the source of interest, from
A standard deviation, assuming that the effect is two-sided, can then be computed based on a uniform, triangular, or normal distribution of possible effects. The convention is to assign infinite degrees of freedom to standard deviations derived in this manner. | ||
