Statistical Sampling
Statistical sampling is the selection of a random, representative subset from a larger lot, record set, or population, allowing conclusions about the whole without inspecting every unit. In supply chain and manufacturing, it supports acceptance sampling, inventory verification, supplier quality checks, and process monitoring by reducing inspection burden while controlling risk.
On a receiving dock, statistical sampling replaces exhaustive checking with a controlled subset: an inspector selects cartons, pallets, coils, or bins at random and compares observed defects against a predefined acceptance threshold tied to lot size and risk. If the threshold is exceeded, the lot is rejected or held for further inspection. In warehousing, the same principle supports cycle counting and inventory audits, sampling records by SKU family or zone to estimate on-hand accuracy without a full physical count. For raw materials feeding production, sample size is set by criticality, so high-risk components receive tighter plans. The method also informs supplier performance and process monitoring, using sampled records to estimate nonconformance rates when full inspection is too slow or costly. The goal is decision-grade evidence about lot quality, record accuracy, or process health while preserving throughput and controlling exposure.
Is statistical sampling the same as random sampling?
No. Random sampling is one method inside statistical sampling; statistical sampling is the broader framework for selecting a subset and making an inference about the population.
Why is statistical sampling used instead of 100% inspection?
Because it reduces inspection cost and cycle time while still enabling risk-based decisions about accepting or rejecting lots.
When is stratified sampling preferred in inventory auditing?
When the population is heterogeneous, such as separating by warehouse zone, SKU family, supplier, or record class so each subgroup is represented in the audit sample.