The **standard deviation** (SD) is a measure of the spread of the data (how far from the normal it is). A number such as the **mean** or the **standard deviation** may be found either for an entire population (symbolized as **σ** or **σ _{x}**) or for a sample (symbolized as

**s**) drawn from that population.

Basically, when your standard deviation, it means that the values in a statistical data set are close to the mean of the data set, on average, and a large standard deviation means that the values in the data set are farther away from the mean, on average.

The **variance** (symbolized s² or σ²) is defined as the average of the squared differences from the mean. In other words, once you find the mean, you subtract the mean from each sample, and square that number. You then take the average of these squared numbers by adding them up and dividing by the number of observations. The formula is shown below for a population:

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- Keep in mind that
**s²**and**n**is used for sample populations while**σ²**and N for the entire population.

Once we have the *variance*, it is a simple matter to get the *standard deviation* (s or σ – notice the lack of the power). As mentioned earlier, SD is the most common measure of dispersion.

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Let’s look at an example where variance and standard deviation are calculated: