# Coefficient of Variance

The coefficient of variation (**CV**) is statistical technique closely tied to standard deviation.
Often otherwise referred to as *relative standard deviation*, the coefficient of variance seeks to describe the distribution of the values
with respect to the mean. This variable is represented as percentage, with a lower percentage indicating a more precise approximation and a
higher percentage being less accurate.

The formula to calculate the coefficient of variance is as follows: **CV = (σ / μ) * 100**

- σ : standard deviation
- μ : mean

Coefficients of variation are used to represent the extent of how much the data points differ in relation to the mean, as well as with respect to each other. There are several advantages to using this technique:

- The CV is relative to the standard deviation of the dataset and should be understood within the context of the dataset. As a result, because we are dealing with a percentage, it is a unitless value, which makes it easier to be compared with other CVs.
- It is common method used in risk assessment tools, as users can go with the lower more accurate CV percentage.
- The CV helps uncover the degree of consistency between results carried out under the same conditions repeatedly. This allows the data analyst to assess both errors in measurements and their levels.

However, this technique is not without its drawbacks:

- The drawback to using this approach is that the CV becomes undefined or infinite, if the mean equals 0.
- Because log(1) = 0, it's pointless to consider variables that are represented as logarithms, as you'll just be expressing 0 in a different form.