Meaning
Quality evaluation systems classify individual product units against discrete pass or fail criteria rather than measuring continuous variable dimensions. In statistical quality control, attribute sampling logic determines whether an entire production lot is accepted or rejected based on the count of nonconforming units found in a randomly selected sample. This binary assessment applies to visual defects and functional presence checks, but stops applying when quantitative measurements like tensile strength or weight variance require variable statistical evaluation.
Binary Classification
Inspection protocols record each unit as conforming or nonconforming against explicit visual or functional specifications. When attribute sampling logic operates within ISO 2859-1 standards, defective units are counted without recording the degree of deviation from the mean. A single severe scratch counts equally alongside a minor cosmetic smudge under a simple pass or fail tally.
Evaluation Rule
Acceptance quality limits specify the maximum allowable number of defective units within a given sample size before the lot faces rejection. Under attribute sampling logic, crossing the predefined acceptance threshold forces the entire batch into hold status. Quality managers compare the observed defect count against published standard tables to issue a pass or fail determination.
Contractual Trigger
Purchase agreements embed these evaluation rules directly into quality assurance schedules to govern lot acceptance at the factory gate. When attribute sampling logic triggers a lot rejection, the buyer gains the legal right to demand re-sorting, full rework, or immediate replacement at the vendor expense. Failure to define these decision boundaries in the purchase order leaves lot disputes open to subjective negotiation.