How Sampling Plans Affect Representativeness of Test Results

Reliable laboratory testing begins before a sample reaches the laboratory. The way a sample is selected, collected, handled and prepared can influence whether the resulting data accurately reflect the material or batch being evaluated. This is why sampling plans are an important part of quality control, analytical testing and evidence-based decision-making.

A well-designed sampling plan defines what will be sampled, where and when samples will be collected, how many sample units are needed, and how the samples will be handled. These decisions help determine whether test results can reasonably be used to draw conclusions about the larger material or population.

1. What Is a Sampling Plan?

A sampling plan is a documented approach for selecting and collecting samples for a particular testing objective. It establishes the procedures and responsibilities involved in obtaining material for analysis.

Depending on the product and purpose of testing, a sampling plan may specify:

The material, batch or population under investigation.
The characteristic or parameter to be measured.
The locations and times of sample collection.
The number of sample units and how they will be selected.
Sample collection tools and procedures.
Sample identification, storage, transport and preparation.
The criteria used to evaluate and interpret results.

According to the National Institute of Standards and Technology (NIST), a sampling plan should be designed around the questions the investigation needs to answer and the parameters that must be measured.

Read the NIST guidance on defining a sampling plan.

2. Why Does Representativeness Matter?

Representativeness describes how well the selected sample reflects the relevant characteristics of the material or population being studied.

For example, a large batch of a chemical material may contain multiple containers or may have variations associated with processing, storage or handling. If a sample is collected only from one convenient location, it may not capture the variation present throughout the batch.

When the sample does not adequately represent the material, the resulting test data may provide an incomplete or misleading picture of its characteristics.

A representative sample does not guarantee a correct result by itself. The analytical method, equipment, sample preparation, laboratory practices and interpretation of data also matter.

3. How Sampling Plans Influence Test Results

A. Selection of sampling locations

The location from which a sample is collected can affect the characteristics observed during testing. For materials that may vary across containers, production stages or storage locations, the sampling plan should consider where variation is likely to occur.

A suitable approach distributes sampling across the relevant parts of the material or process rather than relying solely on the easiest location to access.

B. Number of samples collected

Sample size affects how much information can be obtained about a population and how much sampling variability may remain. However, collecting more samples does not automatically eliminate bias.

The appropriate number of samples depends on factors such as material variability, testing objectives, the desired level of confidence, acceptable decision risk and applicable requirements.

Sample sizes should therefore be justified by the testing objective and a suitable statistical or technical rationale.

C. Sampling method

Different sampling methods serve different purposes. Random sampling can help reduce selection bias when every eligible unit has a suitable chance of selection. Stratified sampling may be useful when a population contains meaningful subgroups, such as different production lots or storage locations.

Systematic sampling can help distribute sample collection across a process or area. The appropriate method depends on the population, the expected pattern of variation and the question being investigated.

The US Environmental Protection Agency’s guide to selecting a sampling design explains how sampling approaches should be matched to the objective and characteristics of the investigation.

D. Sample handling and preparation

Representativeness can be compromised after collection if a sample is contaminated, improperly stored, incorrectly labelled or altered during transport and preparation.

A documented procedure should address relevant factors such as suitable containers, sample identification, storage conditions, handling precautions, preparation methods and transfer to the laboratory.

These controls help preserve sample integrity and maintain traceability from collection through analysis.

4. Sampling Error and Analytical Error Are Different

Understanding the difference between sampling error and analytical error is essential when interpreting test results.

Sampling error arises when the collected sample does not fully reflect the material or population of interest.

Analytical error arises from the measurement process, including factors such as method performance, instrument behaviour, calibration and laboratory execution.

For example, a laboratory may measure the submitted sample precisely, but that result may still fail to describe the entire batch if the original sample was poorly selected.

Consequently, improving laboratory precision alone may not resolve a problem caused by an unrepresentative sample. Sampling and measurement should be considered together when evaluating the reliability of results.

For a deeper technical explanation, consult Eurachem’s guide, Measurement Uncertainty Arising from Sampling.

5. The Importance of Sampling Plans in Chemical and Nicotine-Related Testing

In chemical quality control, sampling is an important part of obtaining meaningful analytical data. The relevance of a sampling plan depends on the material, its physical properties, the testing objective and the characteristics being measured.

For nicotine-related materials, a quality evaluation may involve parameters such as assay, impurities or other product-specific characteristics. The sampling procedure should be appropriate to the material and the intended analysis.

Depending on the material and process, a documented plan may address:

Identification of the product and batch.
Selection of appropriate containers or sampling locations.
Procedures for collecting and combining samples, where justified.
Measures to prevent contamination or sample alteration.
Sample labelling, storage and transport.
Preparation methods appropriate to the analytical procedure.
Traceability between the submitted sample and the reported results.

These are general quality considerations, not a universal sampling protocol for every nicotine product. Specific procedures should be established using applicable product specifications, validated or otherwise suitable methods, relevant standards and regulatory requirements.

For additional background, read McCoy NIC’s article on Nicotine Purity and Assay Results.

6. How to Develop a More Reliable Sampling Plan

A practical sampling plan should begin with a clearly defined objective and a documented understanding of the material being tested.

Step 1: Define the objective. Identify the characteristic to be measured and the decision the test results will support.

Step 2: Define the population. Specify the batch, material, containers, production period or other population to which the results are intended to apply.

Step 3: Assess potential variation. Consider whether material characteristics may vary by location, time, processing stage or container.

Step 4: Select a suitable sampling method. Choose a method that addresses the objective and the expected sources of variation.

Step 5: Justify the sample size. Use an appropriate technical or statistical rationale, taking into account variability and the consequences of an incorrect decision.

Step 6: Document collection and handling. Record the sampling procedure, sample identification, relevant conditions and any deviations.

Step 7: Review the limitations. Interpret the laboratory results in the context of the sampling method, analytical method and intended use of the data.

Step 8: Review and improve the plan. Where appropriate, use historical results, process knowledge and observed variability to determine whether the sampling approach needs revision.

7. What Should a Sampling Report Record?

Clear documentation allows laboratories, quality teams and other stakeholders to understand how the sample was obtained and what the results represent.

Depending on the procedure and testing context, records may include:

Sampling date, location and responsible personnel.
Product, batch or sample identification.
Sampling method and sample quantity.
Number of units or locations sampled.
Relevant environmental or handling conditions.
Storage, transport and preparation details.
Deviations from the documented procedure.
Analytical method and test results.
Limitations relevant to interpretation.

Not every field applies to every test. Documentation should follow the applicable method, quality system and regulatory requirements.

8. Common Sampling Plan Mistakes to Avoid

Several avoidable practices can reduce confidence in test results:

Selecting samples only because they are convenient to access.
Ignoring known differences between containers, locations or production periods.
Choosing sample sizes without a documented rationale.
Using inconsistent collection or preparation procedures.
Failing to preserve sample identity and traceability.
Assuming that precise laboratory measurements prove the original sample was representative.
Applying results beyond the population or conditions that the sampling plan was designed to cover.

Recognising these limitations helps organisations make better-informed decisions about product quality and process performance.