# Standard deviation - formula and example

Observe that in the standard normal curve there are 3 standard deviations σ on each side of the mean μ, The percentages shown are percentages of the area under the curve. The total area is 100%. Knowing the percentage of area between the mean two point on the standard deviation line allows us to calculate the number of scores contained between these points.

### Standard deviation -formula

The formula for standard deviation is: $$σ =\sqrt{\sum{({X}-{\bar{X})}}^2 \over {n-1}}$$(Note that σ is the symbol for standard deviation. It is in reality s, that is the square root of variance).

We read this: Standard deviation is the square root of variance. The symbol for variance is \(s^2\). Variance is the sum of each score from the mean squared, over the sample size minus 1 (n-1). Another way of saying this is: To find variance we subtract each score from the mean and then square this difference. Then we add all of these squared differences and divide by the number of scores minus 1. (Please review the chapter on variance.)

### Standard deviation -practice example

SUMMARY OF THE ABOVE CALCULATIONS

X | $X-\bar{X}$ | $(X-\bar{X})^2$ |
---|---|---|

34 35 39 30 39 40 |
-2.17 -1.17 2.83 -6.17 2.83 3.83 |
4.7089 1.3689 8.0089 38.0689 8.0089 14.6689 |

ΣΧ=217 $\bar{X}=36.17$ |
$\sum{(X-\bar{X})^2}=74.8334 $. |

Now we plug in the calculated values above to the variance formula.

$$s^2 ={\sum{({X}-{\bar{X})}}^2 \over {n-1}}$$

$s^2 ={74.8334 \over {6-1}}=12.47$

**This is the variance of our data.**

Finally The formula for standard deviation is: $$σ =\sqrt{\sum{({X}-{\bar{X})}}^2 \over {n-1}}$$ $$σ =\sqrt{{s}^2 }$$ $$σ =\sqrt{{s}^2 }=\sqrt{12.47}=3.53$$

This is the standard deviation of our data.

Finally The formula for standard deviation is: $$σ =\sqrt{\sum{({X}-{\bar{X})}}^2 \over {n-1}}$$ $$σ =\sqrt{{s}^2 }$$ $$σ =\sqrt{{s}^2 }=\sqrt{12.47}=3.53$$

This is the standard deviation of our data.

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## Comments

## What good is sd for?

What good is sd for?

## Same as variance.

Same as variance.