Standard Deviation Calculator
Calculate mean, variance, and standard deviation from a list of values.
Count
6
Mean
18.6667
Selected standard deviation
4.7188
Selected variance
22.2667
Population standard deviation
4.3076
Sample standard deviation
4.7188
Population variance
18.5556
Sample variance
22.2667
Method used
Sample variance divides by n - 1.
Sorted dataset
Parsed from pasted text, CSV, or table-style input.
| Index | Value |
|---|---|
| 1 | 12 |
| 2 | 15 |
| 3 | 18 |
| 4 | 20 |
| 5 | 22 |
| 6 | 25 |
Histogram preview
Approximate distribution from the parsed dataset.
Formula
Population standard deviation = sqrt(sum((x - μ)^2) / n). Sample standard deviation = sqrt(sum((x - x̄)^2) / (n - 1)).
Calculate mean, variance, and standard deviation from a list of values.
How to Use
Calculate mean, variance, and standard deviation from a list of values. Fill in Values, then review the calculated Count, Mean, Population standard deviation, Sample standard deviation, Population variance, Sample variance, Selected standard deviation, Selected variance, and Method used.
- Open the calculator : Start with Standard Deviation Calculator.
- Enter values : Fill in the required inputs and any optional settings.
- Review the result : Read the output and use the about page for more detail if needed.
Common Questions
What formula does the Standard Deviation Calculator use?
Population standard deviation = ?(sum((x - μ)^2) / n). Sample standard deviation = ?(sum((x - x̄)^2) / (n - 1)).
What does standard deviation actually tell you?
Standard deviation measures the amount of variation or dispersion in a set of values. A low standard deviation means most data points are grouped closely around the average (mean), while a high standard deviation indicates the data is spread out over a much wider range.
What is a normal distribution (bell curve)?
In a normal distribution, roughly 68% of all data points will fall within one standard deviation of the mean, 95% will fall within two standard deviations, and 99.7% will fall within three. This is known as the Empirical Rule.