Readers are referred to the following documents, which contain measures of the agreement: There are several formulas that can be used to calculate the limits of the agreement. The simple formula given in the previous paragraph and well suited to sample sizes greater than 60 is, in statistics, reliability between advisors (also cited under different similar names, such as. B interrater agreement, inter-rated matching, reliability between observers, etc.) the degree of correspondence between the advisors. This is an assessment of the amount of homogeneity or consensus given in the evaluations of different judges. Consider the case of two A and B examiners, who evaluate the response sheets of 20 students in a class and mark each student as “passport” or “fail,” with each examiner reaching half of the students. Table 1 presents three different situations that can occur. In situation 1 in this table, eight students receive a pass score from the two examiners, eight from the examiners a “bad grade” and four from one examiner the pass mark, but the “fail” score of the other (two from A and the other two from B). Thus, the results of the two examiners are the same for 16/20 students (agreement – 16/20 – 0.80, disagreement – 4/20 – 0.20). It looks good. However, it is not taken into account that some notes could have been presumptions and that the agreement could have been reached by chance. ( observed agreement [Po] – expected agreement [Pe]) / (agreement 1 expected [Pe]). In this competition, the judges agreed on 3 out of 5 points.
The approval percentage is 3/5 – 60%. Kalantri et al. considered the accuracy and reliability of the pallor as a tool for detecting anemia.  They concluded that “clinical evaluation of pallor in cases of severe anaemia may exclude and govern modestly.” However, the inter-observer agreement for pallor detection was very poor (Kappa values -0.07 for conjunctiva pallor and 0.20 for tongue pallor), meaning that pallor is an unreliable sign of diagnosis of anemia. By comparing two methods of measurement, it is interesting not only to estimate both the bias and the limits of the agreement between the two methods (interdeccis agreement), but also to evaluate these characteristics for each method itself. It is quite possible that the agreement between two methods is bad simply because one method has broad convergence limits, while the other is narrow. In this case, the method with narrow limits of compliance would be statistically superior, while practical or other considerations could alter that assessment. In any event, what represents narrow or broad boundaries of the agreement or a large or small bias is a practical assessment. Think of two ophthalmologists who measure the pressure of the ophthalmometer with a tonometer. Each patient therefore has two measures – one of each observer. CCI provides an estimate of the overall agreement between these values.
It is akin to a “variance analysis” in that it considers the differences in intermediate pairs expressed as a percentage of the overall variance of the observations (i.e. the overall variability in the “2n” observations, which would be the sum of the differences between pairs and sub-pairs). CCI can take a value of 0 to 1, 0 not agreeing and 1 indicating a perfect match. Subsequent extensions of the approach included versions that could deal with “under-credits” and ordinal scales.  These extensions converge with the intra-class correlation family (ICC), which allows us to estimate reliability for each level of measurement, from the notion (kappa) to the ordinal (or ICC) at the interval (ICC or ordinal kappa) and the ratio (ICC).