Measurement uncertainty explained

Every measurement result is an estimate. Measurement uncertainty is the parameter that describes the spread of values that could reasonably be attributed to the measurand, given everything known about the measurement. It is not the error — the error is unknown — and it is not a tolerance.

Building an uncertainty budget

• List every influence: reference standard, drift, resolution, repeatability, environment, method.
• Quantify each as a standard uncertainty. Type A evaluations use statistics on repeated readings; Type B evaluations use other information such as certificates and specifications, with an assumed distribution (normal, rectangular, triangular).
• Combine the standard uncertainties. For independent inputs the combined standard uncertainty is the root sum of squares.
• Multiply by a coverage factor k to obtain the expanded uncertainty. k = 2 corresponds to approximately 95 % coverage for a normal distribution.

Reading an uncertainty statement

'U = 0.12 °C, k = 2' means the laboratory believes the true value lies within ±0.12 °C of the reported value with roughly 95 % confidence. Without the coverage factor the number is ambiguous. Without knowing whether the instrument's own contribution was included, the number cannot be applied to your measurements.

Why it is the model's job to explain, not to calculate

Combining components is simple arithmetic, but it is exactly the kind of arithmetic a language model gets wrong quietly. In ValiTrac the combination and expansion are performed by a deterministic calculation engine and returned to the model as tool results; the model explains the result and cites the evidence, and any answer that involved a calculation is flagged for human review.