Process Class
Uncertainty derivation methodology utilizes existing data rather than repeated measurements to estimate the doubt associated with a measurement value. During a type b evaluation, technical personnel analyze manufacturer specifications, previous certificates and generic scientific literature to bound the expected error. This is done when direct statistical sampling is not feasible.
Information Source
Experience from previous trials informs the selection of probability distributions. When performing a type b evaluation, common practice assumes a rectangular or triangular distribution unless documentation suggests a different shape. This results in a standard uncertainty value for further math.
Trust relies on the quality of the datasheet.
Application Context
Equipment resolution errors typically sit inside this classification. Because type b evaluation does not require high-speed data acquisition, it allows laboratories to include items like aging and drift coefficients without running new five-year tests. This speeds up the reporting cycle.
Analysts subtract known biases first.
Verification Level
Documentation from the source material must be stored in the uncertainty log. Since a type b evaluation is based on non-measured evidence, auditors look for valid reasons behind the selected distribution choices. Calibration confidence stays high when the input sources are reliable.
Accuracy depends on this informed judgement.