The primary purpose of method validation is to demonstrate that the method is fit for its intended use. Traditionally, an analytical method is deemed valid if its performance characteristics such as accuracy and precision are shown to meet pre-specified acceptance criteria. However, these acceptance criteria are not directly related to the method intended purpose, which is usually a gurantee that a high percentage of the test results of the future samples are close to their true values. Probability-based acceptance criteria have been increasingly used. Such criteria allow for assessing method validity, taking into account the relationship between accuracy and precision. Although several statistical test methods have been proposed in literature to test the “fit for use” hypothesis, the majority of the methods are not designed to protect the risk of accepting unsuitable methods, thus having the potential to cause uncontrolled consumer’s risk. In this talk, we propose a test method based on generalized pivotal quantity (GPQ) inference. Through simulation studies, the performance of the method is compared to five existing approaches. The results show that both the new method and the method based on ß-content tolerance interval with a confidence level of 90%, hereafter referred to as ß-content (0.9), control Type I error, thus consumer’s risk while the other existing methods do not.