Key takeaways
Short answer: Precision and accuracy are often used interchangeably, but in measurement they mean opposite kinds of correctness. Accuracy is closeness to the true value — are you hitting the target? Precision is consistency — do repeated measurements agree with each other, regardless of whether they are on target? The danger is the precise-but-inaccurate gauge: it gives the same reading every time, which feels trustworthy, while being consistently wrong. You need both. For how this connects to keeping instruments correct, see calibration vs verification.
Accuracy is how close a measurement is to the actual true value of what you are measuring. An accurate scale reads 100.0 grams for a true 100-gram mass. Accuracy is about correctness against a known reference: it answers are we hitting the real target. A measurement system can be accurate on average even if individual readings scatter a bit, as long as they centre on the true value. Accuracy problems are problems of bias — a systematic offset that pushes readings consistently high or low. A scale that always reads two grams heavy is inaccurate by a fixed bias, and no amount of repetition will reveal the error if you only look at consistency.
Precision is how close repeated measurements are to one another, independent of whether they are correct. A precise instrument gives nearly the same reading every time you measure the same thing — tight, repeatable, low scatter. Precision is about consistency, not correctness: it answers do we get the same answer each time. Crucially, a measurement can be extremely precise and still be wrong, if all those consistent readings are consistently off the true value. Precision problems are problems of random variation — readings scatter widely around whatever centre they have. A precise but inaccurate gauge is dangerous precisely because its consistency masquerades as reliability.
The classic way to see it is a dartboard. Accurate but imprecise: the darts scatter widely but cluster around the bullseye on average — right target, inconsistent. Precise but inaccurate: the darts land in a tight cluster, but off in one corner — consistent, wrong target. Neither accurate nor precise: scattered and off-centre. Accurate and precise: a tight cluster on the bullseye — what you actually want. The picture makes the key insight obvious: tightness (precision) and centring (accuracy) are independent. A tight cluster tells you nothing about whether you are on target, which is why judging a measurement system by its repeatability alone is a trap.
A shop measures a shaft with a true diameter of 50.00 mm using two gauges. Gauge A reads 50.02, 49.98, 50.01, 49.99 — scattered but averaging 50.00: accurate, not very precise. Gauge B reads 50.21, 50.20, 50.21, 50.20 — beautifully consistent, but averaging 50.205: precise, and precisely wrong by about 0.2 mm. An operator trusting Gauge B because its readings agree would pass or reject parts against a target that is a fifth of a millimetre off, scrapping good parts or shipping bad ones, with total confidence. The fix for Gauge A is reducing variation; the fix for Gauge B is calibration to remove the bias. Same word reliable, two completely different faults.
Diagnosing the type matters because the fixes differ. Poor precision — wide scatter on repeats — points to random variation: a worn gauge, inconsistent technique, environmental noise, an unstable fixture. You attack it by tightening the method and the equipment. Poor accuracy — consistent offset from the true value — points to bias: a calibration that has drifted, a zero error, a systematic setup fault. You attack it by calibrating against a known standard. The trap is using the wrong fix: recalibrating a gauge that is merely imprecise does nothing for the scatter, and trying to reduce variation on a gauge that is precise but biased never finds the offset. Identify which one first.
Measurement quality sits underneath the quality factor of OEE, and it is easy to forget. The quality factor counts good versus defective units — but that count is only as trustworthy as the gauge deciding good from bad. A precise-but-inaccurate measurement system can systematically misjudge conforming parts as scrap (destroying the quality factor for no real reason) or pass true defects (hiding a quality problem until it reaches the customer). Either way, your OEE quality number becomes fiction. Reliable measurement is the unglamorous foundation that makes the quality factor — and the yield and scrap figures behind it — mean anything.
Fabrico is the system that consumes your quality data, so the integrity of that data matters to everything it reports. By making good, rework, and scrap counts visible at the point of production and trending them over time, it helps surface the tell-tale signatures of a measurement problem — a station whose reject rate jumps after a gauge change, or scrap that does not match downstream reality. Good measurement discipline upstream and honest OEE reporting downstream go together. Book a demo to see how reliable quality data drives reliable OEE.
Accuracy is how close a measurement is to the true value; precision is how close repeated measurements are to each other. A measurement can be precise but inaccurate (consistently wrong) or accurate but imprecise (right on average but scattered). You need both.
Yes, and it is a common trap. A gauge that gives the same reading every time but is consistently off the true value is precise but inaccurate. Its consistency makes it feel reliable while it is systematically wrong.
Accurate but imprecise darts scatter around the bullseye; precise but inaccurate darts cluster tightly off-target; accurate and precise darts cluster tightly on the bullseye. It shows that precision (tightness) and accuracy (centring) are independent.
Poor precision (wide scatter) points to random variation — fix the gauge, technique, or environment. Poor accuracy (consistent offset) points to bias — calibrate against a known standard. Using the wrong fix for the wrong fault does not work.
The quality factor of OEE counts good versus defective units, and that count is only as good as the gauge making it. A precise-but-inaccurate system can wrongly scrap good parts or pass real defects, turning your OEE quality number into fiction.
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