Back to Measurement, Uncertainty and Detection Limits

### Need For Uncertainty Evaluation

Every reported measurement result (x) should include an estimate of its overall uncertainty (u(x)), which is based on as nearly a complete assessment as possible.
The uncertainty assessment should include every conceivable or likely source of inaccuracy in the result.
The counting error is only one component to be considered.
The result should be presented as a measured value ± a derived uncertainty.

### Measurand and Measurement Error

Measurand:
The result of a measurement is often used to estimate the value (or quantity) of a certain parameter, for instance the specific activity of a certain component in a laboratory sample.
Error of measurement:
The difference between the measured result and the actual value of the measurand is called the error of the measurement. This error may vary with repetitions of the measurement while the value of the measurand remains fixed.

### Various Error Types

Random errors:
Random effects cause the measured result to vary randomly when the measurement is repeated. This results in a random error
Systematic errors:
Systematic effects cause the result to tend to differ from the value of the measurand by a constant absolute or relative amount, or to vary in a non-random manner.
Spurious errors:
Such errors are those caused by human blunders and instrument malfunctions. They should be avoided by “good laboratory practices”.

### Uncertainty

The error of a measurement is (in most cases) unknowable because one cannot know the error without knowing the true value of the measurand.
However, the uncertainty of a measurement is a concept with practical uses.
The uncertainty of a measurement may be defined as a parameter associated with the result of the measurement that characterises the dispersion of the values that could reasonably be attributed to the measurand. The uncertainty of a measured value thus gives a bound for the likely size of the measurement error.