Standard Deviation

Standard deviation measures the typical distance between data values and the mean. A small standard deviation means the values stay close to the center. A larger one means the data is more spread out. Because the answer stays in the original units, it is usually easier to interpret than variance.

Move the spread slider first, then shift the center, then add an outlier. Watch which changes affect the standard deviation and which ones only move the whole data set.

Standard deviation explorer

Use the same five-point shape, then test three ideas: widening the spread makes the standard deviation grow, shifting every value together keeps it the same, and an outlier can change it fast.

CenterCurrent center: 0SpreadTypical distance pattern: 0, 1, and 2 spread-units from the centerOutlier offsetTurn on the outlier to move one extra point far from the mean.

Include one extra outlier point

FormulaPopulation: sqrt(sum((x - mean)^2) / n)Sample: sqrt(sum((x - mean)^2) / (n - 1))

Number line

Each dot is one value. The red line marks the mean. Standard deviation grows when the dots sit farther from that line.

Current data: -4, -2, 0, 2, 4

SummaryCount: 5Mean: 0Mode: Population standard deviationSum of squared distances: 40Variance: 40 / 5 = 8Standard deviation: 2.828

What to notice

Changing the center shifts the whole group left or right, but it does not change the spread as long as the distances between points stay the same.

An outlier can pull the mean and usually makes the standard deviation larger because one squared distance becomes much bigger than the rest.

Distances from the mean

Value	x - mean	(x - mean)^2
-4	-4	16
-2	-2	4
0	0	0
2	2	4
4	4	16

What Standard Deviation Tells You

A standard deviation of $0$ happens only when every value is the same. Beyond that, there is no universal cutoff for "small" or "large." The number only makes sense relative to the scale of the data set.

For example, a standard deviation of $2$ points may be small on a $100$ -point exam, but a standard deviation of $2$ seconds may be large in a short race. Context matters.

Population Vs. Sample Standard Deviation

Use the population formula only when your data includes the full group you want to describe. If your data is a sample used to estimate a larger population, use the sample formula instead.

For a full population:

\sigma = \sqrt{\frac{1}{N}\sum_{i=1}^{N}(x_i - \mu)^2}

For a sample:

s = \sqrt{\frac{1}{n-1}\sum_{i=1}^{n}(x_i - \bar{x})^2}

That $n-1$ adjustment matters only in the sample case. It corrects for the fact that the sample mean $\bar{x}$ is estimated from the same data.

Worked Example: Same Mean, Different Spread

Compare these two data sets:

Set A: $8, 9, 10, 11, 12$
Set B: $6, 8, 10, 12, 14$

Both have mean $10$ . But Set B is more spread out, so it must have the larger standard deviation.

For Set A, the deviations from the mean are $-2, -1, 0, 1, 2$ . Squaring gives $4, 1, 0, 1, 4$ , which sum to $10$ . If you treat the set as a population, the variance is $10/5 = 2$ , so the standard deviation is

\sqrt{2} \approx 1.41

For Set B, the deviations are $-4, -2, 0, 2, 4$ . Squaring gives $16, 4, 0, 4, 16$ , which sum to $40$ . The population variance is $40/5 = 8$ , so the standard deviation is

\sqrt{8} \approx 2.83

The means match, but the spread does not. That is exactly the job of standard deviation.

What To Notice In The Explorer

Moving every value by the same amount changes the mean, but it does not change the standard deviation.
Pulling values farther from the mean increases the standard deviation.
A single outlier can change the result a lot because larger deviations are squared.

Try Your Own Version

Try your own version in the explorer with two data sets that share the same mean. Keep the center fixed, widen the spread, and check whether the standard deviation changes the way you expect.

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