Efficient sampling by artificial attributes

Hypotheses about the fraction of items in a lot possessing a “specification attribute” X < L can be tested by generally sampling the variable X or directly sampling the attribute of interest. When the process variance is known, it is often more efficient to test against “compressed limits” for one or more “artificial” attributes X < L_a, X < L_b etc. This study discusses the efficient choice of one or two compressed limits. General guidelines for this choice are suggested, and then evaluated under many hypothetical test specifications. One compressed limit offered ˜40% – 97% savings over direct attribute sampling; two limits allowed about 20% further savings.