The phrase "acceptance sampling" is correct and usable in written English.
It is typically used in the context of quality control and statistical sampling methods to determine whether to accept or reject a batch of products based on a sample.
Example: "The company implemented acceptance sampling to ensure that only high-quality products were shipped to customers."
Alternatives: "quality control sampling" or "sampling for acceptance".
For acceptance sampling, the probability of accepting the lot is a cumulative sum containing x = 0, 1, 2, …, c, in the framework of the cumulative probability distribution.
This paper presents a method for designing a fuzzy acceptance sampling plan (FASP) for the case where sampling parameters are defined as fuzzy numbers, in particular for quality inspection of geospatial data with ambiguous quality characteristics.
In the application of acceptance sampling, the producer and consumer predetermine the acceptable values for P FN) and P FP) along with LTPD and AQL.
DR and all posterior literature in acceptance sampling assume that the risk of rejecting acceptable lots has no relevance to the consumer.
The classification rule in traditional acceptance sampling is relatively simple: a lot is acceptable if in a sample of size n the number of defective parts x is less than or equal to a predetermined cutoff value c.
What is unclear in traditional acceptance sampling is the necessity of linking the acceptable quality limit (AQL) exclusively to the producer and the lot quality percent defective (LTPD) exclusively to the consumer.
One method, referred to as acceptance sampling, can be used when a decision must be made to accept or reject a group of parts or items based on the quality found in a sample.
The practice of acceptance sampling determines the values of c and n that reduce to an acceptable level the probability of error.
Therefore, adopting an effective acceptance sampling plan is an important issue.
The quality control is performed using a single acceptance sampling plan by attributes.
Among other results, we show that MNS acceptance sampling plans with smaller sample size and bigger acceptance number perform better than commonly used acceptance sampling plans for attributes based on simple random sampling (SRS) technique.
