common measures of tendency
I concur that understanding the common measures of tendency, outliners, range enables one to make correct interpretations of data. It is also true that there are scenarios in which a set of a particular group of hospitals under a study may lack but a mean is provided. In that case, one should add the known numbers from the data sets and set up an equation. In the given scene, this could be 211, 286, 267, 301, 309, 288, 223, 237 278 adding up to 2091. This is followed by creating an equation with x, the known data set. (2091+x)/10=315. The next step is isolating x by multiplying with ten on both sides and subtracting 20191 from both sides. To get 3150-2091=x. This process will enable finding the value of the tenth data set that is missing.
I also agree that there are some data sets with large values compared to others. As noted, larger data sets have a significant impact on the standard deviation and the mean of a data set. In case one of the values in the data sets is extremely high it most likely leads to skew-ness. Skew-ness affects the standard deviation and the mean of the data set. Notably, this would the mean is larger or greater than the median. Skewed data leads to a high standard deviation indicating a wide-spreading relation of the data making the data unreliable. By understanding skewness, a statistician can understand which data sets that are significant to conduct a study with. Therefore, it advises an administration or hospital on the quality of data and how reliable it is to make conclusions.