Title: Sampling and Sampling Plans
Course: PW-Course-Import-Test > Section: Module 4: Sampling and Calibration

Module 4

A correct sampling is the cornerstone for successful inspection. Since sampling is a statistical estimation of the population, sample size plays an important role. If the sample size is less than what is statistically required then the estimation of the population using the sample is going to be flawed. If the sample size chosen is bigger than what is required, then you spend more time inspecting and handling parts that were not required to be inspected.

What is Sampling?

Sampling refers to the practice of evaluating (inspecting) a portion of the population for the purpose of inferring information about the population. The portion of the population that is inspected is known as a sample and the population is the entire lot of components or product.

Statistically speaking, the properties of the sample distribution are used to infer the properties of the population (lot) distribution. An accept/reject decision for the entire lot is normally made based on the results of the sample. Sampling is an audit practice.

Why Sample?

This is a great question. When the lot size is small and you have the resources for inspection, then one would wonder why sample parts when the entire lot can be inspected. Sampling has it advantages over 100% inspection as stated below:

- Economical
  - Less time required
  - Less inspection labour
- Less handling damages
- Fewer errors
- Very reliable

Sampling Terminology

In this section we will define the various statistical terms used during inspection. There are a lot of terms in here and understanding them is important in understanding the inspection process.

- Population: Any complete group with at least one characteristic in common.
- Sample Size: Quantity of individuals or items selected from the population.
- Sample: A small portion of individuals or items selected from the population.
- Defect: A departure of a quality characteristic from its intended level or state that occurs with a severity sufficient to cause an associated product or service not to satisfy intended normal, or foreseeable, usage requirements.
- Non conformity: Departure of a quality characteristic from its intended level or state that occurs with severity sufficient to cause an associated product or service not to meet a specification requirement.
- Statistics: The practice or science of collecting and analyzing numerical data in large quantities, especially for the purpose of inferring proportions in a whole from those in a representative sample.
- Statistical process control: A method of quality control which uses statistical methods. SPC is applied in order to monitor and control a process. Monitoring and controlling the process ensures that it operates at its full potential.
- Variable: Measured on a continuous scale. Example, time, weight, distance or temperature that can be measured in fractions or decimals.
- Attribute: Data that are counted and cannot have fractions or decimals. Attribute data arise when you are determining only the presence or absence of something: success or failure, correct or not correct.
- Acceptable Quality Level: Quality level that is the worst tolerable. Products or components that exhibit below this quality level must be rejected.

Population vs. Sample

Now let us once again review the difference between a population and a sample. As shown in the figure a sample is a subset of the population or lot. By inspecting the sample we make inference on the population. If the sample has parts that meet all acceptance criteria then we will accept the population. If the sample has part(s) that do not meet the acceptance criteria then we may reject or accept the entire population depending on the sampling plan used.