The objective of a quality system is to provide accurate and reliable results for clinical decision-making. One part of this is Quality Control (QC) validation. QC validation is not routinely applied in veterinary laboratories. This leads to the inappropriate usage of random QC rules without knowing the Probability of error detection (P ) and Probability of false rejection (P ) of a method. In this paper, we will discuss why QC validation is important, when it should be undertaken, why QC validation is done, and why it is not commonly done. We will present the role of total analytical error (TEa) in the QC validation process and the challenges when a consensus TEa has not been published. Finally, we will also discuss the possibilities of 'gray zone' determinations and mention the effects of bias on the quality of results. Reasons for the low prevalence of performing QC validation may include (a) lack of familiarity with the concept, (b) lack of time and resources needed to conduct QC validation, and (c) lack of TEa goal for some measurands. If no TEa is available, the user may elect to use a 'reverse approach' to QC validation. This uses the CV and bias generated from the evaluation of QC measurements, specifying P , P , and N (number of QC measurements/run). This identifies the lowest total error that can be controlled under these defined conditions, thus enabling the laboratory to have an estimate of the 'gray zone' associated with results generated with a specific assay.