Sampling risk

The probability of incorrectly rejecting or accepting a particular lot based on a sample. In acceptance sampling you make a decision to accept or reject an entire lot based on the results from inspecting a sample from that lot. Because your decision to reject or accept the lot is based only on a sample, not on data from the entire lot, you risk rejecting "good" lots and accepting "bad" lots.

For example, you receive a shipment of 10,000 microchips. You have two criteria: sample size = 200 and acceptable quality level (AQL) = 1.5%. If fewer than 8 of the 200 inspected microchips are defective, you will accept the entire shipment. If 8 or more are defective, you will reject the entire shipment.  

Like hypothesis testing, you can make two types of errors while accepting/rejecting a lot. Type I error is referred to as the producer's risk because the producer made a good lot yet it was rejected. Type II error is known as the consumer's risk because the consumer has received a shipment that contains more than an acceptable number of defectives and will result in more waste or rework than anticipated.

The producer's risk is represented by a and the consumer's risk is represented by b.

An operating characteristic curve (OC curve) quantifies these risks on a graph that allows you to choose the appropriate sampling plan for the risks you are willing to incur.