Coefficient of Variation

Standard deviation measures spread in the same units as the original data — rupees, marks, kilograms, whatever the data represents. This makes it impossible to directly compare the relative variability of two data sets measured in different units (say, marks out of 100 versus a company's revenue in lakhs of rupees), or even two data sets in the same unit but with very different average sizes (a standard deviation of ₹5 means something very different for an average price of ₹20 than for an average price of ₹2,000).

The coefficient of variation (CV) solves this by expressing the standard deviation as a percentage of the mean, producing a dimensionless number that can be compared directly across data sets, however different their units or scales:

A smaller CV means more consistent (less variable) data relative to its own average; a larger CV means the data is comparatively more spread out. This makes CV especially useful in business contexts — comparing the consistency of returns from two investment options, the reliability of two machines' output, or the variability of sales performance across two branches — precisely the kind of comparison a bare standard deviation cannot make fairly if the two things being compared have different average sizes. Worked Example 10 below shows exactly this: a data set with the higher average is shown to actually be the less consistent one, once both are converted to CV. …