Mittavirheet

Mittavirheet, or measurement errors, refer to the deviation between a measured value and the true value of the quantity being measured. In metrology, the measurement error E is often expressed as E = X_measured − X_true. Errors are typically categorized as systematic (bias) and random (stochastic); some descriptions also include gross or human errors.

Systematic errors produce a consistent bias that is constant or proportional to the measured quantity and can often be identified and corrected through regular calibration against reference standards. Random errors introduce variability between repeated measurements and affect precision; they are reduced by increasing the number of measurements, improving measurement procedures, and applying statistical analysis.

Measurement uncertainty is a related but distinct concept. It quantifies the doubt about a measurement result, combining all known and potential error sources to provide a range within which the true value is believed to lie. A single measurement error and measurement uncertainty are not the same, but uncertainty captures the overall confidence in the reported value.

Mitigating mittavirheet involves regular calibration and maintenance of instruments to ensure traceability to standards, adherence to validated measurement procedures, environmental control, and proper operator training. Statistical methods, including repeated measurements and outlier analysis, help estimate and reduce random errors. In practice, laboratories use frameworks such as the Guide to the Expression of Uncertainty in Measurement (GUM), and standards like ISO 5725, ISO 17025, and measurement-system analysis (MSA) to assess accuracy, precision, and uncertainty. Mittavirheet are an inherent part of measurement, but their impact can be minimized and quantified.