SigmaCalc

Standard Deviation Calculator

σ and s from raw numbers.

How spread out is your data?

Standard deviation measures the typical distance each value sits from the mean — a small standard deviation means values cluster tightly; a large one means they're spread out widely.

Sample vs. population — why it matters

Dividing by n−1 instead of n (Bessel's correction) is used when your data is a sample meant to estimate a larger population — it slightly inflates the result to correct for the fact that a sample tends to underestimate true variability.

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SigmaCalc

Standard Deviation Calculator

σ and s from raw numbers.

Dataset (comma / space / new-line separated)

Count: 10
Mean μ: 12.7
Sample σ (n−1): 4.449
Population σ (n): 4.22
Variance (sample): 19.789
Variance (population): 17.81

What you entered

Dataset: 10 values

1
Count of values
n= 10
2
Mean
sum ÷ n= 12.7
3
Sum of squared deviations from the mean
Σ(xᵢ − 12.7)²= 178.1
4
Sample variance (÷ n−1)
178.1 ÷ 9= 19.7889
5
Sample standard deviation
√19.7889= 4.4485
6
Population variance (÷ n)
178.1 ÷ 10= 17.81
7
Population standard deviation
√17.81= 4.2202

Result

Sample standard deviation: 4.449

With a mean of 12.7, this dataset has a sample standard deviation of 4.449 — most values fall within about that distance of the mean.

How spread out is your data?

Standard deviation measures the typical distance each value sits from the mean — a small standard deviation means values cluster tightly; a large one means they're spread out widely.