# Compactness of a distribution

I calculate the input to a box and whisker diagram like this (data is a sorted integer array), and LQ, UQ are the lower resp. upper quantile:

    Min:    data[0],
LQ:     data[len(data)/4],
Median: data[len(data)/2],
UQ:     data[len(data)/4*3],
Max:    data[len(data)-1],


My question would be: Is there a mathematical definition / algorithm to define how 'compact' such an array of integers is? If Max-Min = 0 it's super compact, but what would a sane definition of compactness / heterogeneity be if I compare two such arrays and for both holds Max - Min != 0 ?

Normal distribution of data is assumed!

You can use the coefficient of variation (CV) to compare the homogeneity/ variability between two data sets. The CV is given by $$CV=\frac{s}{\bar{x}}$$ where $s$ denotes the standard deviation of the sample and $\bar{x}$ the mean of the sample. The $CV$ is measured in percent units, which is means that is a number (free of specific measurement units) and can be used for comparison between different data sets. Sets with values of CV less than $10\%$ tend to be interpreted as homogenous sets.
In the example you mention, i.e. when $\max -\min=0$ then the CV will also be zero and that is also the only case in which the CV will be zero.