Measurement Uncertainty and Laboratory Comparison: Essential Concepts for Quality Professionals

In daily quality work, "measurement uncertainty" and "laboratory comparison" are frequently mentioned, but what exactly do they mean? To what extent does a quality manager need to understand them? This article explains these concepts in the most concise way possible.

What is Measurement Uncertainty?

Measurement Uncertainty is a quantitative description of the reliability of a measurement result. Simply put: any measurement has errors, and uncertainty tells you "within what range the result might fluctuate."

Key Points:

• Uncertainty ≠ Error. Error is the difference between the measured value and the true value (always unknown), while uncertainty is the range within which the measured value might be distributed (can be estimated).
• Sources of Uncertainty: instrument accuracy (the main factor), environmental temperature and humidity, operator technique, sample uniformity, etc.
• Representation: For example, "length = 25.4 mm ± 0.1 mm (k=2)," where ±0.1 is the expanded uncertainty, and k=2 indicates a confidence level of approximately 95%.

Why Do Quality Professionals Need to Understand Uncertainty?

1. Determining Conformance/Nonconformance: When the measurement value is close to the specification limit, uncertainty determines whether your judgment is "confident." For instance, a measurement value of 25.4 mm, with a tolerance of 25.0～25.5 mm and an uncertainty of 0.1 mm—strictly speaking, a measurement of 25.45 mm cannot be directly judged as conforming.
2. Extension of MSA: Gauge Repeatability and Reproducibility (GR&R) is strongly related to uncertainty. Good uncertainty assessments are often based on MSA data.
3. Requirement for Laboratory Accreditation: ISO/IEC 17025 explicitly requires laboratories to have the capability to assess and report measurement uncertainty.

What is Laboratory Comparison?

Laboratory Comparison (Interlaboratory Comparison) involves distributing the same test sample to multiple laboratories and comparing the results for consistency. The purpose is straightforward: to see how your laboratory's test results compare to those of your peers.

Common Forms:

• Proficiency Testing (PT): Organized by authoritative institutions, with fixed frequencies (usually 1-2 times per year), and formal scoring (z-value). z-value ≤ 2 is passing, 2 < z ≤ 3 is a warning, and z > 3 is failing.
• Interlaboratory Comparison (Informal): Organized independently between laboratories, not constrained by accreditation bodies, but still valuable for reference.

Basic Steps for Comparison:

1. Select stable comparison samples (must be uniform, stable, and able to support the entire comparison period).
2. Each laboratory independently tests under specified conditions.
3. Aggregate data → calculate the assigned value (usually using a robust mean or median).
4. Calculate the z-value or En-value for each laboratory.
5. Analyze deviations and take corrective actions.

The Relationship Between Uncertainty and Comparison

The two concepts complement each other:

Practical Experience in the Workplace: If a laboratory's evaluated uncertainty is significantly smaller than the deviation between the comparison result and the assigned value, it suggests that the uncertainty assessment is overly optimistic and needs to be re-evaluated. Conversely, if the deviation is much smaller than the evaluated uncertainty, it indicates that the assessment is overly conservative and not sufficiently precise.

Quick Start Tips for Quality Professionals

• No Need to Calculate Uncertainty Yourself—These tasks are performed by metrology calibration personnel or laboratory engineers. The quality professional's responsibility is to understand the reports and the impact on decision-making.
• Pay Attention to "Abnormal" Comparison Results: Laboratories with z-values ≥ 3 must initiate corrective actions, and following up on the progress of these actions is the responsibility of the quality department.
• Incorporate Uncertainty into Conformance Criteria: It is recommended to clearly define "acceptance criteria considering uncertainty" in inspection specifications (i.e., "judgment rules") to avoid disputes.

Summary

Measurement uncertainty and laboratory comparison are the two cornerstones of the ISO/IEC 17025 system. Uncertainty tells you how reliable the results are, while comparison tells you how much your laboratory deviates from others. As a quality professional, you don't need to delve into the calculations, but you should be able to understand uncertainty reports, monitor comparison results, and incorporate uncertainty into the inspection judgment process.

Knowledge code: 11.2.2

Version: v20260522

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