The main difference between this ICC correlation and the interclass correlation (Pearson) is that the data are aggregated to estimate the average and variance. The reason is that in the parameter where an intra-cicicular correlation is desired, the pairs are considered disordered. For example, if we look at the resemblance of twins, there is generally no reasonable way to categorize the values for both individuals within a pair of twins. Like the interclassicular correlation, the intraclassicular correlation for coupled data is limited to the interval [-1, 1]. If we assess the absolute match of a result index measured several times, variability must be counted, because the factor is considered a random factor, as in the following equation: Dieclass-Correlations coefficients-Tabelle yields two coefficients with their respective confidence interval of 95%. As a result, Fisher  introduced an intraclassical correlation formula similar to the r interclass correlation coefficient. However, if the number of subjects is very large (well spoken, infinite), it turns out that the intraclass correlation coefficient of Model 1 is simply indicated by  (2), i.e. the variance of the actual score among the subjects of the population P, divided by the overall variance. Early work on intra-light correlations focused on the case of quality measurements and the first CCI statistics (Intraclass Correlation) were changes in interclassical correlation (Pearson correlation). which can be interpreted as a fraction of the overall difference due to variations between groups. In his classic book, Ronald Fisher devotes an entire chapter to the intra-classical correlation of statistical methods for research workers.  In statistics, intraclass correlation or intraclass correlation coefficient (CCI)  is a descriptive statistic that can be used when quantitative measurements are made on group-organized units.
He describes how similar the units of the same group are. Although it is considered a kind of correlation, unlike most other correlation indicators, it works on data structured in groups and not on data structured as twin observations. In general, we should expect that the confidence intervals received from Group A and Group B of 95% of the ICC (A, 1) will overlap. As part of this overlap, we can expect to find the intraclass coefficient of model 2A population 2A, as shown in point 4.1. The only difference with Eq (9) is that the nc2 variance was omitted because of the distortion in the measurements. One can notice the resemblance to Model 1 (see figure 1 or Eq (2)). The coefficient defined by Eq (13) is generally referred to as a measure of consistency between measures , i.e. the extent to which subjects retain their hierarchy and internal differences. It should be noted that Model 2, given by Eq (8), is still adopted. Thus, although the same model is used and the same matrix of measurement data is analyzed, the question asked (coherence rather than concordance) is different. As a term is missing in the denominator, compared to Eq (9), we expect a higher ICC with Eq (13).
It is reasonable from the unatithivable point of view that if we are satisfied with fair consistency and not absolute agreement, the method could also be considered satisfactory, that is, reliable, but in a limited sense. It was noted that Eq (13) is not correctly an intra-class correlation coefficient, as variance in the denominator is not the total variant . Nevertheless, it has been suggested that it could be useful in measuring consistency .