Standard Guide for Measurement Systems Analysis (MSA)

Abstract

4.1 Many types of measurements are made routinely in research organizations, business and industry, and government and academic agencies. Typically, data are generated from experimental effort or as observational studies. From such data, management decisions are made that may have wide-reaching social, economic, and political impact. Data and decision-making go hand in hand and that is why the quality of any measurement is important. This guide presents selected concepts and methods useful for describing and understanding the measurement process. This guide is not intended to be a comprehensive survey of this topic or address specific applications. 4.2 Any measurement result will be said to originate from a measurement process or system. The measurement process will consist of a number of input variables and general conditions that affect the final value of the measurement. The process variables, hardware and software and their properties, and the human effort required to obtain a measurement constitute the measurement process. 4.3 A measurement process will have several properties that characterize the effect of the input variables and general conditions on the measurement results. The properties of a measurement system are discussed in Section 5. 4.4 Measurement System Analysis (MSA) is a broad class of methods used to study the properties of measurement systems, either individually, or some relevant subset of properties taken collectively. In studying one or more of these properties, the final result is some assessment of the capability of the measurement system with respect to the property under investigation. Capability may be cast in several ways, and this may also be application dependent. Section 6 discuses basic MSA methods. Note 1: Sample statistics discussed in this guide are as described in Practice E2586; control chart methodologies are as described in Practice E2587.