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Statistical sampling has been a very popular topic in recent years. The sampling strategy is an important part of statistical sampling. Manufacturing organizations find the following sampling strategies to be useful:

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QM 1123 CLMN John Vandenbemden Random sampling
(Simple) Random sampling (SRS) - Each item in the lot has the same probability to be selected for the sample.
QM 1123 CLMN John Vandenbemden Convenience sampling
Convenience sampling (CS) - Items that are most convenient and easy to select are taken.
QM 1123 CLMN John Vandenbemden Systematic Sampling
c) Systematic Sampling (SyS) - The items in the sample are systematically (by time, number, etc.) spread over the lot.
QM 1123 CLMN John Vandenbemden Stratified sampling
d) Stratified sampling (StS) - The lot is divided into sub lots (strata) from which (random) samples are taken. Usually sub lots and sub lot samples have equal size, but also different sizes of sub lots and/or samples are feasible.
QM 1123 CLMN John Vandenbemden Cluster sampling
e) Cluster sampling (ClS) - The lot is divided into sub lots (cluster) from which (randomly) sub lots are selected. For these selected sub lots all items are sampled (100% inspection).

 

The examples illustrate the different sampling strategies for a two-dimensional population, such as products spread open for a drying process. There is no statistical guidance on which sampling strategy is superior to the others. ISO 2859-4 (1999) advises: “The items selected for the sample shall be drawn from the lot by simple random sampling …. However, when the lot consists of sub-lots or strata, identified by some rational criterion, stratified sampling shall be used in such way that the size of the subsample from each sublot or stratum is proportional to the size of that sublot or stratum”.