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The measurand is the quantity intended to be measured, specified with enough detail about the system, conditions and averaging convention to distinguish it from instrument output or a different derived quantity.

measurement accuracy concerns closeness to the value of the specified measurand; high repeatability alone does not establish it. calibration, an adequate response model and assessment of residual systematic effects are required alongside statistical precision.

A systematic error shifts results in a reproducible or correlated way relative to the measurand. Examples include zero offsets, gain miscalibration, nonlinearity, drift, geometrical misalignment, loading, background contamination and an inadequate response model. Repeated measurements under unchanged conditions can reduce independent noise without exposing such a shift; a small standard error is not proof of measurement accuracy.

Use traceable standards and independent measurement methods, test zero and span, reverse the physical signal where possible, and vary the conditions that could cause the bias. For common-offset cancellation by signal reversal, take measurements $y_{+}=S+b$ and $y_{-}=-S+b$. The half-difference estimates $S$ while the half-sum diagnoses an unchanged offset $b$. Drift between the two readings would violate that cancellation, so alternate or interleave measurements. Randomizing acquisition order separates slow drift from controlled parameter changes; blanks and calibration samples test backgrounds and instrument gain. Blinding helps reduce analysis choices that follow an expected answer.

Corrections have uncertainties and may be correlated across data. Include residual offsets and gains as nuisance parameters or propagate their calibration covariance, rather than treating a common calibration error as fresh independent noise for each datum. Document assumptions and check stability with controlled perturbations. These methods suppress or quantify specified systematic effects; they cannot guarantee elimination of every unknown bias.