The standard deviation formula is an equation to measure the positive square root of the variance, or the average variability; in other words, it measures the average of how far each value in a set of data is from the mean of the data set. The formula indicates how spread-out the numbers are in a data set.

Standard deviation is a useful measure of spread for normal distribution, which is when data is symmetrically distributed with no skew. There are multiple formulas for calculating standard deviation. For example, there are different equations for measuring within an entire population as opposed to a sample set. High standard deviation indicates that data values are further from the mean; low standard deviation indicates the dispersion of data points are all closer to the mean.

A formula for calculating standard deviation is as follows:

s=√1n−1∑ni=1(xi−x̅)2

In this formula, “x” equals the value in the data distribution, “n” equals the sample size, “s” equals the sample standard deviation, and “x̅” equals the sample mean value.

If you don’t have a standard deviation calculator, uses these steps to find the sample variance out of a number of data points:

1. Find the mean. The first step in calculating standard deviation is to calculate the arithmetic mean of your observed data points. The mean is also known as the average value in a set of sample data.

2. Find the deviations from the mean. Subtract the mean from each value in your data set to calculate its deviation from the mean.

3. Find the squared differences from the mean. Next, multiply each value of deviation from the mean by itself, or in other words, square them. The results should all be positive integers.

4. Add the sum of the squared values. Find the sum of squares by adding up all the positive integers that you just calculated.

5. Find the variance value. To find the variance value, divide the sum of the squares by the number of values in the sample.

6. Calculate the square root of the variance. To find the standard deviation, find the square root of the variance value.