Population Standard Deviation Calculator
Compute the population standard deviation of a dataset.
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Introduction
Population Standard Deviation Calculator — Compute the population standard deviation of a dataset. Enter Dataset (comma-separated) to get an instant, accurate result.
Formula
Population standard deviation: σ = √(Σ(x−mean)² / N), the square root of the population variance.
Step-by-step
- Enter the Dataset (comma-separated).
- Click Calculate to see your result instantly.
Real-world example
Example: With Dataset (comma-separated) = 2, 4, 4, 4, 5, 5, 7, 9, the Population Standard Deviation Calculator gives Population Std Dev: 2, Mean: 5.
Frequently Asked Questions
When is population standard deviation correct to use?
Only when your dataset represents every member of the group you care about, not a sample drawn from a larger population.
How does this differ from sample standard deviation?
It divides the sum of squared deviations by N instead of N−1, which gives a slightly smaller value than the sample version.
What inputs does the Population Standard Deviation Calculator need?
You'll need to provide: Dataset (comma-separated). All fields use sensible defaults, so you can see a working example immediately and then adjust the values to match your own numbers.
How accurate is the Population Standard Deviation Calculator?
The Population Standard Deviation Calculator applies the formula exactly as calculated — Population standard deviation: σ = √(Σ(x−mean)² / N), the square root of the population variance. — so results are precise for the inputs you provide. Accuracy depends on entering correct, realistic input values.
Is the Population Standard Deviation Calculator free to use?
Yes, the Population Standard Deviation Calculator is completely free, requires no signup, and runs instantly in your browser.
About the Population Standard Deviation Calculator
The Population Standard Deviation Calculator uses a real, verifiable formula — Population standard deviation: σ = √(Σ(x−mean)² / N), the square root of the population variance. — so results are accurate every time, not an approximation.