The usual statistic for summarising the result would be the mean. For example, a lecture might be rated as 1 (poor) to 5 (excellent). Similarly, the mean from ordered categorical variables can be more useful than the median, if the ordered categories can be given meaningful scores. However, the mean gives the proportion of males in the group, whereas the median merely tells us which group contained more than 50% of the people.
This is clearly not Normally distributed. Consider a variable that takes the value 1 for males and 0 for females. Alas this is not so: if the data are Normally distributed the mean and the median will be close if the data are not Normally distributed then both the mean and the median may give useful information. It is a commonly held misapprehension that for Normally distributed data one uses the mean, and for non-Normally distributed data one uses the median. It is usually nonsensical to use the coefficient of variation as a measure of between subject variability. It has the advantage of being independent of the units of measurement, but also numerous theoretical disadvantages. It is often quoted as a measure of repeatability for biochemical assays, when an assay is carried out on several occasions on the same sample. The coefficient of variation(CV%) is the intrasubject standard deviation divided by the mean, expressed as a percentage.
Single observations on individuals clearly contain a mixture of intersubject and intrasubject variation. If many observations were made on each individual, and the average taken, then we can assume that the intrasubject variability has been averaged out and the variation in the average values is due solely to the intersubject variability. If the observations are close together in time, this standard deviation is often described as the measurement error.Measurements made on different subjects vary according to between subject, or intersubject, variability. This is within subject, or intrasubject, variability and we can calculate a standard deviation of these observations. If repeated measurements are made of, say, blood pressure on an individual, these measurements are likely to vary.