Key takeaways
Short answer: Accuracy and trueness are distinguished in formal metrology (ISO terms), where everyday language often blurs them. In the ISO sense, accuracy is the whole picture — closeness to the true value combining both trueness and precision. Trueness is just the systematic part — how close the average of many measurements is to the true value, i.e. the absence of bias. Precision is the random part — how close repeated measurements are to each other. So accuracy equals trueness plus precision. Everyday usage often calls trueness accuracy, but the formal definitions separate them. For the everyday pair, see precision vs accuracy.
In everyday language — and in the common precision vs accuracy framing — accuracy usually means closeness to the true value (hitting the target), as distinct from precision (consistency, the darts clustering tightly). That everyday usage is useful and widely understood. But in formal metrology, codified in ISO standards, the terms are defined more carefully, and accuracy means something broader than the everyday sense — it splits into two components, with a specific word, trueness, for the part everyday language usually calls accuracy. This is a genuine source of confusion: the formal ISO definitions do not quite match common usage, so accuracy can mean the everyday closeness-to-target or the formal whole-picture-combining-trueness-and-precision depending on context. Understanding the formal definitions clears it up — and matters in metrology, calibration, and quality contexts where the ISO terms are used precisely.
In the formal ISO sense, accuracy is the closeness of agreement between a measurement and the true value, considering both systematic and random error together — the whole picture. Crucially, ISO accuracy combines two components: trueness (the systematic part) and precision (the random part). A measurement system is accurate, in this formal sense, only when it is both true (unbiased — its average hits the true value) and precise (consistent — its repeated readings agree). Accuracy is therefore the overall quality of a measurement, encompassing both whether it is centered on the truth and whether it is tight. This is broader than the everyday meaning, which usually refers only to the centering (what ISO calls trueness). In the formal framework, accuracy is the umbrella: it is the combined result of being both true and precise, and it is degraded by either systematic error (bias, hurting trueness) or random error (scatter, hurting precision).
Trueness is the formal ISO term for the systematic part of accuracy: the closeness of the average of a large number of measurements to the true value. In other words, trueness is about bias — if you measure the same thing many times and average the results, how close is that average to the truth? High trueness means little or no systematic bias (the average is on target); low trueness means a systematic offset (the average is consistently high or low). Trueness deliberately looks at the average, not individual readings, because it is isolating the systematic component — the bias that affects all readings the same way — and averaging out the random scatter. Trueness is what everyday language usually means by accuracy (closeness to target), but the formal framework gives it its own name to distinguish it from the broader ISO accuracy that also includes precision. Trueness is the unbiased-ness of a measurement system: is it centered on the truth, ignoring scatter?
The three terms fit together cleanly in the ISO framework: accuracy is the combination of trueness and precision. Trueness is the systematic component (closeness of the average to the true value — the absence of bias); precision is the random component (closeness of repeated measurements to each other — the absence of scatter); and accuracy is the overall closeness to the true value that depends on both. A measurement can fail accuracy in two ways: poor trueness (a systematic bias pulling the average off-target, even if the readings are tight) or poor precision (so much random scatter that individual readings stray from the truth, even if the average is on-target). To be accurate in the full ISO sense, a measurement system must be both true (unbiased) and precise (consistent). This maps onto the dartboard picture: trueness is whether the darts center on the bullseye on average (no bias), precision is whether they cluster tightly (low scatter), and accuracy is both — a tight cluster on the bullseye. The formal split simply gives separate names to the two independent ways a measurement can be good or bad.
Consider three measurement systems weighing a true 100.0 gram mass many times. System A: the readings scatter widely but average to 100.0 — it has good trueness (the average is on-target, no bias) but poor precision (wide scatter). System B: the readings cluster tightly but average to 103.0 — it has good precision (tight) but poor trueness (a 3-gram systematic bias). System C: the readings cluster tightly and average to 100.0 — it has both good trueness and good precision, and therefore good accuracy in the full ISO sense. Only System C is accurate by the formal definition, because accuracy requires both trueness and precision. System A is true but imprecise; System B is precise but not true; neither is fully accurate. Everyday language might call System A accurate (its average is on-target) — but in ISO terms it has good trueness, not good accuracy, because its precision is poor. The formal terms separate the two qualities that everyday accuracy blurs together.
Like precision and accuracy generally, trueness and accuracy sit beneath the trustworthiness of the data feeding the quality factor of OEE. The quality factor counts good versus defective units, and that judgement depends on a measurement system that is both true (unbiased) and precise — fully accurate in the ISO sense. A system with poor trueness (a systematic bias) can systematically misjudge conforming parts as defective or vice versa, exactly as a precise-but-inaccurate gauge does, corrupting the OEE quality number. The remedy for poor trueness is calibration and adjustment to remove the bias, while poor precision is addressed differently (the gauge, the method) — and the formal split between trueness and precision is exactly what tells you which problem you have. Trustworthy measurement — both true and precise — is the foundation of an honest OEE quality factor.
Fabrico consumes the quality data your measurement systems produce, so their trueness and precision flow into the reliability of its OEE. By trending good-versus-defective results, it can help surface the signatures of a measurement problem — for instance a systematic shift in reject rate after a gauge change that suggests a trueness (bias) issue, as opposed to erratic scatter that suggests a precision problem. Reliable measurement upstream — both true and precise — and honest OEE downstream go together. Book a demo to see how trustworthy measurement drives trustworthy OEE.
In formal ISO metrology, accuracy is the combination of trueness and precision — closeness to the true value considering both systematic and random error. Trueness is just the systematic part — how close the average of many measurements is to the true value (the absence of bias). Accuracy needs both trueness and precision.
Not in formal ISO terms. Everyday language often equates accuracy with trueness (closeness to target), but ISO defines accuracy as the whole picture combining trueness and precision. Trueness is only the systematic, unbiased part; full accuracy also requires precision.
Trueness is the closeness of the average of many measurements to the true value — essentially the absence of systematic bias. It looks at the average (not individual readings) to isolate the systematic component, averaging out random scatter. High trueness means the average is on-target.
Accuracy combines trueness and precision. Trueness is the systematic component (average close to true value, no bias); precision is the random component (repeated readings close to each other, low scatter); accuracy is the overall closeness to the true value, which requires both being true and being precise.
The OEE quality factor counts good versus defective units, which depends on a measurement system that is both true and precise. Poor trueness (systematic bias) can systematically misjudge parts, corrupting the quality number. Calibration and adjustment fix trueness; the formal split shows which problem you have.
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