Once it is established that the bug measurement system is an attribute measurement system, the next step is to look at the notions of accuracy and precision in relation to the situation. First, it helps to understand that precision and precision are terms borrowed from the world of continuous measuring instruments (or variables). For example, it is desirable for the tachometer in a car to read the right speed over a speed range (for example.B. 25 mph, 40 mph, 55 mph and 70 mph), no matter who reads it. The absence of distortions over a range of values over time can generally be described as precision (on average, the distortion can be considered erroneous). The ability of different people to interpret and tune the same value of Ness multiple times is referred to as accuracy (and accuracy problems may stem from a problem with Ness, not necessarily from the people who use it). An attribute agreement analysis allows the impact of repeatability and reproducibility on accuracy to be assessed simultaneously. It allows the analyst to study the responses of multiple auditors, while examining multiple scenarios. It compiles statistics that assess the ability of evaluators to agree with themselves (repeatability), with each other (reproducibility) and with a well-known control or accuracy value (overall precision) for each characteristic – again and again. As with any measurement system, the accuracy and precision of the database must be understood before the information is used (or at least used during use) to make decisions. At first glance, it would seem that the apparent starting point is an attribute analysis (or the measurement of R&R attributes). But it may not be such a good idea.
For example, if repeatability is the main problem, evaluators are confused or undecided on certain criteria. If reproducibility is the problem, then evaluators have strong opinions on certain conditions, but those opinions differ. If the problems are shown by several evaluators, the problems are systemic or procedural. If the problems concern only a few evaluators, the problems may simply require a little personal attention. In both cases, training or work aids could be adapted either to specific individuals or to all evaluators, depending on the number of evaluators guilty of imprecise attribution of attributes. Repeatability and reproducibility are elements of precision in an analysis of the attribute measurement system, and it is advisable to first determine whether or not there is a precision problem. This means that before designing an analysis of the attribute agreement and choosing the appropriate scenarios, it is essential that an analyst consider a database check to determine whether past events have been correctly coded or not. In this example, a repeatability assessment is used to illustrate the idea and it also applies to reproducibility.
The point here is that many samples are needed to detect differences in an attribute analysis, and if the number of samples is doubled from 50 to 100, the test does not become much more sensitive. Of course, the difference that needs to be identified depends on the situation and the level of risk that the analyst is willing to assume in his decision, but the reality is that, in 50 scenarios, it will be difficult for an analyst to think that there is a statistical difference in the reproducibility of two evaluators with match rates of 96% and 86%. With 100 scenarios, the analyst will hardly be able to see a difference between 96 and 88 percent. .