The quality of measurement data is defined by the statistical properties of multiple measurements obtained from a measurement system operating under stable conditions. For instance, suppose that a measurement system, operating under stable conditions, is used to obtain several measurements of a certain characteristic. If the measurements are all “close” to the master value for the characteristic, then the quality of the data is said to be “high”.Similarly, if some, or all, of the measurements are “far away” from the master value, then the quality of the data is said to be “low”.
The statistical properties most commonly used to characterize the quality of data are the bias and variance of the measurement system. The property called bias refers to the location of the data relative to a reference (master) value, and the property called variance refers to the spread of the data.
One of the most common reasons for low-quality data is too much variation. Much of the variation in a set of measurements may be due to the interaction between the measurement system and its environment. For instance, a measurement system used to measure the volume of liquid in a tank may be sensitive to the ambient temperature of the environment in which it is used.
In that case, variation in the data may be due either to changes in the volume or to changes in the ambient temperature. That makes interpreting the data more difficult and the measurement system, therefore, less desirable.
If the interaction generates too much variation, then the quality of the data may be so low that the data are not useful. For example, a measurement system with a large amount of variation may not be appropriate for use in analyzing a manufacturing process because the measurement system’s variation may mask the variation in the manufacturing process. Much of the work of managing a measurement system is directed at monitoring and controlling variation. Among other things, this means that emphasis needs to
be placed on learning how the measurement system interacts with its environment so that only data of acceptable quality are generated.
source of Quality of Measurement Data: Analysis of measurement systems
