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2. Measurement Process Characterization
2.5. Uncertainty analysis
2.5.3. Type A evaluations

Type A evaluations of random components

Type A evaluations of random components Type A sources of uncertainty fall into three main categories:
  1. Uncertainties that reveal themselves over time
  2. Uncertainties caused by specific conditions of measurement
  3. Uncertainties caused by material inhomogeneities
Time-dependent changes are a primary source of random errors One of the most important indicators of random error is time, with the root cause perhaps being environmental changes over time. Three levels of time-dependent effects are discussed in this section.
Many possible configurations may exist in a laboratory for making measurements Other sources of uncertainty are related to measurement configurations within the laboratory. Measurements on test items are usually made on a single day, with a single operator, on a single instrument, etc. If the intent of the uncertainty is to characterize all measurements made in the laboratory, the uncertainty should account for any differences due to:
  1. instruments
  2. operators
  3. geometries
  4. other
Examples of causes of differences within a laboratory Examples of causes of differences within a well-maintained laboratory are:
  1. Differences among instruments for measurements of derived units, such as sheet resistance of silicon, where the instruments cannot be directly calibrated to a reference base
  2. Differences among operators for optical measurements that are not automated and depend strongly on operator sightings
  3. Differences among geometrical or electrical configurations of the instrumentation
Calibrated instruments do not fall in this class Calibrated instruments do not normally fall in this class because uncertainties associated with the instrument's calibration are reported as type B evaluations, and the instruments in the laboratory should agree within the calibration uncertainties. Instruments whose responses are not directly calibrated to the defined unit are candidates for type A evaluations. This covers situations in which the measurement is defined by a test procedure or standard practice using a specific instrument type.
Evaluation depends on the context for the uncertainty How these differences are treated depends primarily on the context for the uncertainty statement. The differences, depending on the context, will be treated either as random differences, or as bias differences.
Uncertainties due to inhomogeneities Artifacts, electrical devices, and chemical substances, etc. can be inhomogeneous relative to the quantity that is being characterized by the measurement process. If this fact is known beforehand, it may be possible to measure the artifact very carefully at a specific site and then direct the user to also measure at this site. In this case, there is no contribution to measurement uncertainty from inhomogeneity.

However, this is not always possible, and measurements may be destructive. As an example, compositions of chemical compounds may vary from bottle to bottle. If the reported value for the lot is established from measurements on a few bottles drawn at random from the lot, this variability must be taken into account in the uncertainty statement.

Methods for testing for inhomogeneity and assessing the appropriate uncertainty are discussed on another page.

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