### ISO/IEC 17025:2017 & Decision Rule

New ISO 17025 and Decision Rule

By definition, sampling involves selecting a portion of material (i.e. sample or samples) from a substance, material, product or even a consignment of goods to represent or provide information about that larger body of material (i.e. *population*).

Although the ISO/IEC 17025 accreditation standards and also its latest revision are still concerned about the technical competence of organizations that conduct laboratory activities, it is reckoned that the reliability of testing result lies on how representative the sample analyzed is for the bulk material of interest. As the saying goes : “*The result is not better than the sample that it is based on*”.

A question has thus been asked: Can sampling be considered as a stand alone activity or should sampling activities always be associated with testing or calibration?

It is obvious that although the scope of this accreditation standards is with laboratory activities, sampling indeed has an inevitable connection to the laboratory’s analytical process that produces the test result which is important to the end users. It is usually impossible to analyze the whole bulk (or lot) of material (statistically called ‘population’ or ‘sampling target’). Therefore, proper sampling plays an essential role to ensure the validity of the final test result.

The FDIS requires laboratory to have a sampling plan and documented procedures for sampling in their field of testing. The laboratory is allowed to state “analyzed as received” in the report if it has not been responsible for the sampling stage. Also, laboratory performing sampling or testing activities shall evaluate measurement uncertainty, *i.e*. the uncertainty of sampling process is to be evaluated and forms an additional uncertainty contributor to the measurement uncertainty evaluation of the whole testing or calibration process.

Even though a laboratory does not get involved in the sampling of a population outside its premises, it often carries out sub-sampling process before the start of the analytical procedure. Therefore the subject of sampling cannot be ignored also in such laboratory.

As said earlier, since the main purpose of laboratory analysis is to estimate the value of analyte concentration in a sampling target, sample taken should be as representative of the sampling target under study as possible. This is to ensure that the property that each sample has the ** same probability** of being drawn from the population as another sample.

So, in order to optimize the whole measurement process including taking a good sample for analysis, the sampling planner needs to gather information of the sampling target and to decide on appropriate sampling protocols.

Ask the following questions during information gathering:

- What are the analyte(s) to be determined?
- Is the measurand in the bulk material homogeneously or heterogeneously distributed?
- What is the kind of average sample required: hourly, daily, by shift, batch, shipment, ?
- Are all the necessary personnel and equipment available?
- What is the uncertainty level of the analyte allowed in the specification, if any? –
*this information is particularly important for deciding on the number of samples to be taken for analysis*

To decide on the appropriate sampling protocols, one must try to deal with the following subjects:

- Manual vs automatic sampling
- Sampling frequency – number of samples to be drawn
- Sample sizes (volume, weight)
- Number of samples to be drawn
- Sampling locations (ship’s tanks, silos, warehouses)
- Individual vs composite samples
- Which sampling strategy
- Random sampling
- Stratified random sampling
- Systematic sampling

We shall discuss these sampling strategies in more details in the next blog.

- Design of Experiments
- ANOVA
- Randomization
- Linear regression
- Significance testing
- outliers
- Sampling
- Probability distribution
- uncertainty
- GUM
- Sampling statistics
- Probability
- Microbiology
- Degrees of freedom
- Median
- Monte Carlo
- Measurement error
- F-test
- Confidence interval
- propagation of uncertainty
- p-Value
- Control chart
- Anderson-Darling
- ISO 17025
- Decision Rule
- t-test
- Confidence limits
- Normal distribution
- IQR
- Variance
- Law of Averages
- Coverage factor
- How to
- ISO FDIS 17025
- Risk
- Cross-checks
- Detection limit
- Factorial design
- Chi-square
- interlab comparison
- Central limit theorem
- quartile
- Divisor
- Type I and II errors
- Precision
- Accuracy
- Aerobic plate count
- Compliance
- hypothesis testing

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