If one wants to see how much variance there is in the responses that various subjects give to a single rating measure, why are measures any more sophisticated than a mere box plot, or error bar with SEMs needed? What do measures such as the Inter-rater reliability tell us, above and beyond simple descriptive statistics of the response distribution?

Am I right that the difference lies in the assumption we make about the "true" value of the thing being rated, in other words that if we use the Inter-rater Reliability we assume that "ideal" subjects would agree and their scores would come close to an objectively-true/measurable value, whereas if using e.g. a box plot, that asuumption need not be made?

If one wants to see how much variance there is in the responses that various subjects give to a single rating measure, why are measures any more sophisticated than a mere box plot, or error bar with SEMs needed? What do measures such as the Inter-rater reliability tell us, above and beyond simple descriptive statistics of the response distribution?

Am I right that the difference lies in the assumption we make about the "true" value of the thing being rated, in other words that if we use the Inter-rater Reliability we assume that "ideal" subjects would agree and their scores would come close to an objectively-true/measurable value, whereas if using e.g. a box plot, that asuumption need not be made?

Is a high inter-rater reliability not simply (qualitatively) equivalent to a low standard deviation/error of the distribution?