Useful Tips for Measurement and Uncertainty in ISO 17025

Measurement and Uncertainty in ISO 17025

Discussions about an audacious goal in the laboratory world, especially from a regulatory standpoint, may seem inconsequential. Measurement and Uncertainty in ISO 17025 Standard may not seem to have a noticeable impact on our day-to-day life. But it does. The use of uncertainty measurements renders information calculable and allows for more precise decisions. In uncertain environments, it is even more important to increase data collection, verify traces of possible correlation or causation among results (or tests), share data and results with others in the organization (particularly internally), and elevate problem management.

Measurement and uncertainty in ISO 17025 are considered vital. The 2017 revision of ISO/IEC 17025 introduced a requirement that laboratories account for measurement uncertainty when reporting results compared to a specification or requirement.

The standard requires that uncertainty of measurement is understood, documented, and applied to all quantitative procedures as per clause 7.6. Labs must take necessary steps to estimate the uncertainty of measuring all significant processes within an activity, no matter how minor. This requires that uncertainty components are identified and evaluated for each method. In such instances where validation data is collected during verification, this could be used to determine the uncertainty of measurement. One must remember that errors and uncertainties can have multiple sources: Instruments, item or sample itself, measurement process, calibration method, human, environment, and sampling.

What is Measurement and Uncertainty in ISO 17025?

The science of measurement is commonly known as ‘metrology’.  When something is measured, the act of measurement causes a shift in the outcome. For instance, the accuracy of the measurement will be limited by the equipment’s resolution. As a result, an instrument with a resolution of 0.1 cannot measure an amount that is truly 0.665; it can only indicate 0.6 or 0.7. As a result, measurement uncertainty exists. Simply holding a steel rule will cause it to heat up, increasing its total length and creating tiny variations or “uncertainties.”

Basic terms to remember…

  • Measurement – Measurements comprise two components: the value and its units. These values come from analysis activities such as mass, time, volume etc.
  • Measurand – This is a specific quantity subject to measurement.
  • Uncertainty of measurement – This deals with the quality of measurement. There is no exact value; instead, the value is as accurate as what it is measured with. This can therefore be defined as the most probable value.
  • Error – This is the difference between measured and actual values. Often, errors may be confused with the uncertainty of measurement, but in fact, are different. In simple terms, the error is the ‘flaws’ in the measuring process, while uncertainty is the ‘doubt’ of the result measurement. Therefore we can conclude that a series of flaws will help us quantify the doubt.


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Different Types of Measurement and Uncertainty in ISO 17025?

    • Random – Random uncertainty occurs when different unexpected results are achieved after repeating an analysis.

    • Systematic – Systematic uncertainty is when the value achieved is more or less than the actual value.

    • Standard uncertainty – The uncertainty of the measurement’s outcome expressed as a standard deviation.

    • Combined standard uncertainty – The estimated standard deviation equivalent to the summation of the square root of the total variance obtained by adding all the uncertainty components, commonly referred to as error propagation.

    • Bias – The difference between the expected outcome of testing and the real value

The uncertainty of measurement is established by:

    1. Define the quantity to be measured to get a proper estimate for uncertainty.

    1. Identify the source(s) of the uncertainty of measurement. Such sources include Sampling; Sample preparation; Environmental conditions; Human error; Equipment; Reference material used. These errors can be combined at the validation stage to establish a combined uncertainty.

There are numerous instances where minor variables and constants influence repeatable measurements. As a result, when repeated measurements are conducted, the observed findings cluster around the actual real value. This population can be referred to as the ‘statistical population’. Most of the measure’s results will be near the true value, but some will deviate. Most frequently, but not consistently, the set of repeated outcomes will follow a statistical curve, also known as a distribution. This curve represents the statistical dispersion of all possible results and serves as a gauge of how likely it is that the reported result will be close to the actual proper number.

When comparing results that are close to requirements, competence is required in the application of the calculated confidence of the measured result.

Such validations can be carried out by using the below guidelines of measurement and uncertainty in ISO 17025:

    • Decide the desired output, and establish any criteria required. Special attention needs to be taken to the limit of necessary detection and any dilutions needed;

    • Carry out measurements with a significant amount of replicates. A good number of replicates would be 11 readings carried out in duplicates. In this case, the bigger the sample pool and the more operators involved, the better;

    • List each input’s uncertainty that may affect the result. Such inputs include equipment error, volume dispensing error, environmental conditions etc.;

    • From the list, determine what errors will affect the measuring process and if the errors are independent of each other. If the errors are dependent, combined uncertainty calculations may need to be considered;

    • Calculate the errors and necessary calculations required;

    • Evaluate the combined uncertainty, including all independent processes;

    • Calculate uncertainty and note its level of significance;

    • Quote measurement results along the respectively calculated uncertainty and ensure that it fits the set criteria.

The statement of uncertainty should be based on a thorough assessment of the sources of error and uncertainty in the laboratory’s measurement process. This assessment should consider factors such as the precision and accuracy of the measurement equipment, the skill and training of the laboratory staff, and any potential sources of contamination or bias in the sample or the measurement process.

Once the sources of error and uncertainty have been identified and assessed, the laboratory should use accepted statistical methods to calculate a statement of uncertainty for each type of measurement that it performs. This statement should be reported along with the test results and should provide a measure of the confidence that the laboratory has in the accuracy of its measurements.

By providing a statement of uncertainty for its test results, a laboratory can demonstrate its commitment to quality and reliability and can provide its customers with the information they need to make informed decisions based on the test results. This is essential to meeting the requirements of ISO 17025 and providing reliable testing and calibration services.

Want to learn more about Measurement and Uncertainty in ISO 17025?

Luke Desira is an ISO management system consultant whose purpose is to simply and effectively make your company outshine others! Learn how to achieve ISO 17025 accreditation here! To further learn about Measurement and Uncertainty in ISO 17025 and ISO 17025 Laboratory Sampling Requirements, click here!

All management systems based on ISO Standards that are implemented should pertain directly to the organization’s objectives, and ISO 17025 – Testing and Calibration Laboratories should be no different. Have a look at the ISO Certification specialised by Industry to understand in which category your organization falls.

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