A few words on sampling
- What is sampling
Sampling is a process of selecting a portion of material (statistically termed as ‘population’) to represent or provide information about a larger body or material. It is essential for the whole testing and calibration processes.
The old ISO/IEC 17025:2005 standard defines sampling as “a defined procedure whereby a part of a substance, material or product is taken to provide for testing or calibration of a representative sample of the whole. Sampling may also be required by the appropriate specification for which the substance, material or product is to be tested or calibrated. In certain cases (e.g. forensic analysis), the sample may not be representative but is determined by availability.”
In other words, sampling, in general, should be carried out in random manner but so-called judgement sampling is also allowed in specific cases. This judgement sampling approach involves using knowledge about the material to be sampled and about the reason for sampling, to select specific samples for testing. For example, an insurance loss adjuster acting on behalf of a cargo insurance company to inspect a shipment of damaged cargo during transit will apply a judgement sampling procedure by selecting the worst damaged samples from the lot in order to determine the cause of damage.
2. Types of samples to be differentiated
Field sample Random sample(s) taken from the material in the field. Several random samples can be drawn and compositing the samples is done in the field before sending it to the laboratory for analysis
Laboratory sample Sample(s) as prepared for sending to the laboratory, intended for inspection or testing.
Test sample A sub-sample, which is a selected portion of the laboratory sample, taken for laboratory analysis.
3. Principles of sampling
Generally speaking, random sampling is a method of selection whereby each possible member of a population has an equal chance of being selected so that unintended bias can be minimized. It provides an unbiased estimate of the population parameters on interest (e.g. mean), normally in terms of analyte concentration.
“Representative” refers to something like “sufficiently like the population to allow inferences about the population”. By taking a single sample through any random process may not be necessary to have representative composition of the bulk. It is entirely possible that the composition of a particular sample randomly selected may be completely unlike the bulk composition, unless the population is very homogeneous in its composition distribution (such as drinking water).
Remember the saying that the test result is no better than the sample that it is based upon. Sample taken for analysis should be as representative of the sampling target as possible. Therefore, we must take the sampling variance into serious consideration. The larger the sampling variance, the more likely it is that the individual samples will be very different from the bulk.
Hence, in practice, we must carry out representative sampling which involves obtaining samples which are not only unbiased, but which also have sufficiently small variance for the task in hand. In other words, we need to decide on the number of random samples to be collected in the field to provide smaller sampling variance in addition to choosing randomization procedures that provide unbiased results. This is normally decided upon information such as the specification limits and uncertainty expected.
Often it is useful to combine a collection of field samples into a single homogenized laboratory sample for analysis. The measured value for the composite laboratory sample is then taken as an estimate of the mean value for the bulk material.
It is important to note also that the importance of a sound sub-sampling process in the laboratory cannot be over emphasized. Hence, there must be a SOP prepared to guide the laboratory analyst to draw the test sample for measurement from the sample that arrives at the laboratory.
4. Sampling uncertainty
Today, sampling uncertainty is recognized as an important contributor to the measurement uncertainty associated with the reported results.
It is to be noted that sampling uncertainty cannot be estimated as a standalone identity. The analytical uncertainty has to be evaluated at the same time. For a fairly homogeneous population, a one-factor ANOVA (Analysis of Variance) method will be suffice to estimate the overall measurement uncertainty based on the between- and within-sample variance. See https://consultglp.com/2018/02/19/a-worked-example-to-estimate-sampling-precision-measuremen-uncertainty/
However, for heterogeneous population such as soil in a contaminated land, sample location variance in addition to sampling variance to be taken into account. More complicated calculations involve the application of the two-way ANOVA technique. An EURACHEM’s worked example can be found at the website: https://consultglp.com/2017/10/10/verifying-eurachems-example-a1-on-sampling-uncertainty/