Nevertheless, important guidelines have appeared in the literature. Perhaps the first Landis and Koch stated that the values < 0 were unseable and 0–0.20 as light, 0.21–0.40 as just, 0.41–0.60 as moderate, 0.61–0.80 as a substantial agreement and 0.81–1 almost perfect. However, these guidelines are not universally accepted; Landis and Koch did not provide evidence, but relied on personal opinion. It was found that these guidelines could be more harmful than useful.  Fleiss‘:218 Equally arbitrary guidelines characterize Kappas beyond 0.75 as excellent, 0.40 to 0.75 as just to good and less than 0.40 bad. Cohens coefficient Kappa () is a statistic used to measure reliability between advisors (and also the reliability of inter-raters) for qualitative (categorical) elements.  It is generally accepted that this is a more robust indicator than a simple percentage of the agreement calculation, since the possibility of a random agreement is taken into account. There are controversies around Cohens Kappa because of the difficulty of interpreting the indications of the agreement. Some researchers have suggested that it is easier, conceptually, to assess differences of opinion between objects.  For more details, see Restrictions.
Once your company has a more agile contract analysis and the results are displayed in your KPI, you may feel more confident about taking on new business obligations. Setting up new offers will be much easier if you have your entire contract library at your fingertips during negotiation. You can then work more efficiently and take bolder steps in growing your business. In this example, a repeatability assessment is used to illustrate the idea, and it also applies to reproducibility. The fact is that many samples are needed to detect differences in an analysis of the attribute, 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 prepared to bear in the decision, but the reality is that in 50 scenarios, it is difficult for an analyst to think that there is a statistical difference in the reproducibility of two examiners with match rates of 96 percent and 86 percent. With 100 scenarios, the analyst will not be able to see any difference between 96% and 88%. Repeatability and reproducibility are components of accuracy in an analysis of the attribute measurement system, and it is advisable to first determine if there is a precision problem.