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Result, error and uncertainty

R Dybkaer1

  • 1Department of Clinical Chemistry, Frederiksberg Hospital, Denmark.

Scandinavian Journal of Clinical and Laboratory Investigation
|April 1, 1995
PubMed
Summary
This summary is machine-generated.

New guidelines clarify measurement uncertainty. The corrected measured value is the best estimate, with uncertainty components derived from input variances, replacing older methods summing systematic and random errors.

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Area of Science:

  • Metrology
  • Measurement Science
  • Statistical Analysis

Background:

  • International standardization bodies provide new frameworks for measurement value and uncertainty.
  • Understanding components like readings, calibrators, corrections, and influence quantities is crucial.
  • Traditional methods for calculating uncertainty are being updated.

Purpose of the Study:

  • To explain the philosophical and statistical basis for current measurement uncertainty.
  • To introduce updated nomenclature for describing measurement components.
  • To detail the revised methodology for calculating combined standard uncertainty.

Main Methods:

  • Describing the measured value as a function of input quantities.
  • Identifying components of the corrected value: true value, biases, and errors.

Related Experiment Videos

  • Utilizing statistically or non-statistically derived variances for uncertainty components.
  • Combining input variances according to the function relating them.

    • Main Results:

    • The corrected measured value is the best estimate.
    • Uncertainty components are derived from input variances.
    • Combined standard uncertainty is the square root of the combined variance.
    • Expanded uncertainty is obtained by multiplying combined standard uncertainty by a coverage factor.

    Conclusions:

    • The revised approach provides a more robust method for quantifying measurement uncertainty.
    • Updated statistical and philosophical underpinnings enhance measurement science.
    • Accurate reporting of measurement uncertainty is essential for reliable scientific and industrial applications.