The Standard Deviation (Sample), or s, equation computes a most common measure of variability in sample data set. This samples version of the  standard deviation is used to estimate the unknown population's `sigma`, a measure of the amount of variation or dispersion of a set of data values.

What does the standard deviation tell us?

A low standard deviation indicates that the data points tend to be close to the mean  of the data set, that the data adheres to the central tendency to be close to the mean and not to vary wildly from the mean/  

A high standard deviation indicates that the data is spread over a wider range of values.

The Math of the Standard Deviation

The standard deviation of a data set or statistical population is the square root of its variance. The standard deviation, unlike the variance, is expressed in the same units as the data.

The standard deviation (`sigma`)  expressed as the population SD (`sigma`) is:

`sigma = sqrt((sum_(i=1)^N(X_i - barX))/N)`

This means we are calculating the SD on the whole of the data set, the whole population.

See Also

• Try the Mean calculation
• Try the Median calculation
• Try the Mode calculation
• Here's the Range of a data set
• Here's the Min calculation
• And the Max calculation
• Standard Deviation (Sample)
• Standard Deviation (Population)
• Frequency Distribution between min and max
• Frequency Distribution for population
• Try the z-score calculation on your data set
• Try the z-score when you know both mean and SD