Key takeaways
Short answer: Attribute data is categorical — pass or fail, go or no-go, a count of defects. Variable data is measured on a continuous scale — length, weight, temperature. Variable data reveals how close to the limit you are running, enabling early warning before defects occur, and it needs much smaller samples for the same statistical power. Attribute data is simpler to collect but blind until a part fails. See also control chart vs run chart.
Attribute data answers yes or no. It records whether a part passed or failed, or counts how many defects occurred. It is simple and quick to collect — a tick or a count — but it carries no information about how good or how marginal each part was.
Variable data is a measurement on a continuous scale — the actual dimension, weight or temperature. It shows exactly where each part sits within the specification, which is what makes it so much more powerful for control and early warning.
A shaft has a diameter spec of 10.00 +/- 0.05 mm. Attribute data records only "pass" until a part finally measures 10.06 and fails — by which point the process has been drifting for an hour and produced a batch near the edge. Variable data measures the actual diameter on each check: 10.01, 10.02, 10.03... the trend toward the limit is obvious long before any part fails, so the operator adjusts the process while everything is still good. Same parts, but one method warns and the other only reports the failure.
A pass/fail check tells you nothing until a part fails. A measurement shows a trend creeping toward the limit while parts are still good, so you can act before defects happen. It also needs far smaller samples to detect a shift, making it more efficient as well as more informative.
Attribute data is right when measurement is impractical or the characteristic is genuinely categorical — present or absent, correct or incorrect, a visual blemish. Otherwise, prefer variable data for its early-warning power and sample efficiency.
1. Using attribute data where measurement is possible. You give up early warning and sample efficiency.
2. Bigger attribute samples to compensate. Attribute data needs far larger samples for the same power.
3. No SPC on variable data. Collecting measurements but never charting the trend.
4. Pass/fail on a critical dimension. The process drifts unseen until a part finally fails.
Variable data drives real SPC, which catches Performance and Quality drift before it becomes downtime or scrap — feeding OEE improvement upstream of the failure rather than counting it afterward.
Fabrico captures measured quality data so you can chart trends and act before parts fail, not just count rejects. Book a demo to see variable-data quality in your OEE.
Variable data — far fewer for the same statistical power.
It gives early warning before defects occur, while parts are still good.
No — it fits genuinely categorical checks where measurement is impractical.
Variable data drives the most useful statistical process control.
When the characteristic is truly categorical, like present/absent or a visual blemish.
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