Variance vs Standard Deviation



Hey guys,

We know that variance is just the square of standard deviation. Then why the use of having two metrics in statistics?

Also, where do we use SD and Variance? What insights do they help infer from the data?



The variance of a data set measures the mathematical dispersion of the given data relative to the average. Nevertheless, despite this value is theoretically correct, it is very difficult to implement in a real-world case since the values used to compute it have been squared. The SD, as the square root of the variance gives a value that is in the same units as the original values, which makes it much more easier to work with and easy to interpret.

I hope this helps.