Standard Deviation Calculator

Enter your numbers separated by commas, spaces, or new lines. Get mean, variance, standard deviation, and step-by-step work.

Last updated April 2026
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What Is Standard Deviation?

Standard deviation measures how spread out a set of numbers is from the mean (average). A low standard deviation means data points cluster close to the mean. A high standard deviation means they are spread over a wider range. It is one of the most fundamental concepts in statistics and is used in science, finance, quality control, and everyday data analysis.

SD = sqrt[ Σ(xi - x̄)² / (n - 1) ]
Where xi = each value, x̄ = mean, n = number of values

How to Use This Calculator

Enter your data set as comma-separated numbers. The calculator shows the mean, population standard deviation, sample standard deviation, variance, range, and count. It also displays a visual distribution of your data. Use sample standard deviation (n-1) when your data is a subset of a larger population. Use population standard deviation (n) when your data includes the entire population.

Population vs. Sample

The difference is in the denominator: population SD divides by n; sample SD divides by (n-1). The (n-1) correction (called Bessel's correction) adjusts for the fact that a sample tends to underestimate population variability. For large data sets (n > 30), the difference is small. For small samples, it matters. If you measured the heights of every student in a class, that is population data. If you measured 30 students from a school of 1,000, that is a sample.

Standard Deviation FAQ

What is a "good" standard deviation?
There is no universal answer because SD depends on context and units. A SD of 5 on test scores out of 100 means the class performed very consistently. The same SD on incomes measured in thousands would be very tight. SD is most useful for comparing variability between similar data sets or for understanding how far individual values deviate from the mean.
What does the 68-95-99.7 rule mean?
For normally distributed data (bell curve), approximately 68% of values fall within 1 standard deviation of the mean, 95% within 2 standard deviations, and 99.7% within 3. This is called the empirical rule and is fundamental to quality control, scientific measurement, and probability assessment.

Interpreting Standard Deviation

Standard deviation measures how spread out data points are from the average. The empirical rule (68-95-99.7) states that for normally distributed data: 68% of values fall within 1 standard deviation of the mean, 95% within 2, and 99.7% within 3. This has practical applications: if a class's test scores have a mean of 75 and standard deviation of 10, roughly 68% of students scored between 65 and 85. In quality control, the "six sigma" methodology aims for processes where defects occur more than 6 standard deviations from the target, translating to 3.4 defects per million opportunities. In investing, standard deviation measures volatility: a stock with 20% annual returns and 15% standard deviation will see returns between 5% and 35% about 68% of the time.