Measurement error

Measurement error is the difference between a measured value and a reference value. It can arise from random variation, systematic bias, or both.

Accuracy and precision: Accuracy describes closeness to a reference value; precision describes agreement among repeated measurements. Measurement error concerns accuracy, while repeated readings reveal precision.
Random error: Unpredictable variation in measurement results under repeated, nominally identical conditions. Random error makes repeated measurements scatter around a central value.
Error propagation: The calculation of how input uncertainties or errors affect a result derived from several quantities. Derived quantities inherit error from the measurements used to calculate them.
Residual: The difference between an observed value and a value predicted by a statistical model. A residual uses a model prediction as its comparator, not necessarily a reference value.
Measurand: The quantity intended to be measured. Error is defined relative to the quantity a measurement is meant to represent.
Systematic error: A persistent, reproducible deviation that shifts measurement results in a consistent direction or pattern. Systematic error displaces results from the reference in a recurring way.
Regression dilution: The attenuation of an estimated relationship caused by measurement error in an explanatory variable. Noisy measurements can make estimated associations appear weaker than they are.
Mistake: An avoidable incorrect action or judgment that produces an unintended result. Measurement error can occur without a mistake, through unavoidable variation or instrument limits.
Measurement uncertainty: A parameter describing the dispersion of values that could reasonably be attributed to a measurand. Uncertainty quantifies doubt about a result; it is not itself the error.
Instrumental error: Measurement deviation caused by an instrument's design, condition, or calibration. Instrument behavior can create a repeatable offset or distort readings.

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