Acceptance Sampling Common Fallacies

Common fallacies of lot acceptance sampling schemes.

Fallacy 1

Using a “c=0” (accept a sample with only zero defects) rule with an AQL sampling plan means that there will be no defects in the lot/batch.
  • Remember that we are making the decision whether to accept the lot by making an inference to the lot from the sample results.
  • Know what the O-C curve for the resulting plan looks like.

Fallacy 2

Taking a sample based on the percentage of the total lot size offers consistent protection.

Fallacy 3

When using a single sampling plan, if the lot is rejected based on results of the sample, then it is OK to take another sample and call it "double sampling".
  • This approach is based on the “continue to sample and test until you pass” scheme.
  • Legitimate methods exist for allowing double or multiple samples under certain defined conditions. These are published in the sampling standards, such as Mil-Std-105E, ANSI Z1.4, ISO 2859, or BS 6000.

Fallacy 4

It is OK to adjust the sample size of a sampling plan, but still use the “Ac” (“accept”) and “Re” (“reject”) values associated with the plan.
  • The result is an O-C curve with unknown properties. The O-C curve can be fitted, but until this is done the amount of risk associated with the modified sampling plan is unknown.

Fallacy 5

The AQL value represents the highest level of nonconforming material that the customer will experience.
  • The AQL value is only an index that is used to organize the various sampling plans.
  • The AOQL is the sampling plan characteristic that represents the Average Outgoing Quality Level for a specific sampling plan.
  • For AQL-based sampling plans, the AQL value is more closely associated with Pa = 95% and Type 1 error (producer’s risk … the risk of rejecting an acceptable lot).
  • For protection for the customer, it is more useful to focus on Pa = 10% (or another, similar benchmark) to assess Type 2 error (consumer’s risk … the risk of accepting an unacceptable lot).