This paper provides an overview of key methods for assessing the quality of measurement systems and measurement results. Fundamental concepts are analyzed and their definitions compared across various metrological standards. Particular emphasis is placed on the issue of terminological inconsistency, as the same terms are interpreted differently in different documents, which may lead to misinterpretation or incorrect application of methods. This paper also presents practical examples of how these methods are applied to real data. Clear indications are provided regarding the conditions under which each method is recommended, depending on factors such as model complexity, data availability, and intended application. In addition, it offers a discussion and recommendations for future directions, highlighting the need for harmonized terminology, standardization of evaluation procedures, and the adoption of advanced technologies such as artificial intelligence and machine learning in the assessment of measurement quality.
Razumić et al. (Wed,) studied this question.