We compared the performance of Fleiss` K and Krippendorff Alpha as a measure of reliability between rats. Both coefficients are very flexible because they can deal with two or more tips and categories. In both our simulation and case study, the point estimates of Fleiss` K and Krippendorff Alpha were very similar and were not associated with overestimation or underestimation. The asymptomatic confidence interval for Fleiss` K resulted in a very low probability of coverage, while the standard bootstrap interval yielded very similar and valid results for Fleiss`K and Krippendorffs Alpha. The limits of the asymptomatic confidence interval are related to the fact that the underlying normal asymptomatic distribution applies only to the assumption that the true Fleiss` K is zero. For zero-shifted assumptions (we simulated actual values between 0.4 and 0.93), the standard error is no longer appropriate [18, 23]. Because bootstrap confidence intervals are not based on underlying distribution assumptions, they offer a better approach where determining the standard good error for certain assumptions is not straight [24-26]. Kappa`s P value is rarely reported, probably because even relatively low Kappa values may differ by zero, but not large enough to satisfy investigators. [8]:66 Nevertheless, its default error has been described[9] and is calculated by different computer programs.

[10] A case sometimes considered a problem with Kappa Cohens occurs when comparing Kappa, which were calculated for two pairs with the two counselors in each pair who have the same percentage of agreement, but one pair gives a similar number of reviews in each class, while the other pair gives a very different number of reviews in each class. [7] (In the following cases, in the first case, 70 votes in for and 30 against, but these figures are reversed in the second case.) For example, in the following two cases, there is an equal agreement between A and B (60 out of 100 in both cases) with respect to matching in each class, so we expect Cohens Kappa`s relative values to reflect that. Cohens Kappa calculation for each: On the other hand, reliability within the rate is an assessment given by the same person in several instances. The reliability of the interrater and the intra-rater is one aspect of the validity of the tests. Their assessments are useful in fine-tuning the instruments given to human judges, for example by establishing whether a given scale is adapted to measure a given variable. If different evaluators do not agree, either the scale is defective or the advisors must be recycled. Kappa is a way to measure agreements or reliability and to correct the frequency with which ratings might consent to chance. Cohens Kappa,[5] who works for two councillors, and Fleiss` Kappa,[6] an adaptation that works for any fixed number of councillors, improve the common likelihood that they would take into account the amount of agreement that could be expected by chance. The original versions suffered from the same problem as the probability of joints, as they treat the data as nominal and assume that the evaluations have no natural nature; if the data does have a rank (ordinal measurement value), this information is not fully taken into account in the measurements.