Acceptance Sampling

TL;DR

Inspect a sample from a batch to decide whether to accept or reject the whole batch.
Saves time and resources versus inspecting every item, but depends on sample representativeness and statistical assumptions.

Definition

Acceptance sampling is a statistical method used to evaluate the quality of a product or batch of goods. It involves taking a sample of items from the batch and evaluating them to determine if they meet the required standards, allowing an organization to decide whether to accept or reject the entire batch based on the sample results.

Explanation

Acceptance sampling works by randomly selecting and inspecting a subset of items from a production lot or service batch. The sampled items are evaluated for defects, contaminants, or compliance with required standards. If the proportion of nonconforming items in the sample is within predefined acceptable limits, the organization may accept the entire batch; if it exceeds those limits, the batch may be rejected. The method is commonly used to make quick, resource-efficient quality decisions and to inform ongoing quality control efforts.

Advantages described in the source:

Enables rapid decisions on large batches by evaluating only a sample rather than every item.
Reduces the risk of accepting defective or substandard products by using defined acceptable defect/contaminant levels.
Supports improvement of quality control processes through regular sampling and identification of areas needing change.

Examples

Manufacturing (widgets)

A company producing widgets may randomly select a sample of widgets from a batch and inspect them for defects. If the percentage of defects in the sample is below the acceptable level, the company can assume the entire batch meets quality standards and ship it to customers.

Food industry (cans of soup)

A food manufacturer may randomly select a sample of cans from a batch and test them for harmful bacteria. If the percentage of contaminated cans in the sample is below the acceptable level, the manufacturer can assume the entire batch is safe for consumption and sell it to customers.

Service industry (hotel rooms)

A hotel may randomly select a sample of guest rooms from a batch and inspect them for cleanliness. If the percentage of clean rooms in the sample is above the acceptable level, the hotel can assume the entire batch of rooms meets cleanliness standards and offer them to guests.

Use cases

Manufacturing quality control
Food safety testing
Service quality checks (for example, hotel room cleanliness)

Notes or pitfalls

Relies on statistical assumptions and may not always provide accurate results.
Small sample sizes may fail to represent the entire batch.
May be unsuitable for certain product or service types; for example, when the product or service is highly homogenous, sample variation may be too low to yield meaningful results.
Conducting acceptance sampling on every batch may be impractical due to time and resource constraints.

Quality control
Sampling
Statistical assumptions