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Mean (x)-
Sum of Squares (SS)-
Variance (σ2 / s2)-
Std Deviation (σ / s)-
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About

Statistical variance quantifies how spread out a data set is relative to its mean. Financial analysts use this metric to assess asset volatility and risk. Quality control engineers rely on it to measure manufacturing consistency. A low variance indicates data points are clustered closely around the average. A high variance suggests scattered data and less predictability. Distinguishing between population and sample data is critical for accuracy. Using population formulas on sample data underestimates the true spread. This tool applies the Bessel correction automatically when sample mode is selected.

statistics variance standard deviation data analysis variability

Formulas

The calculation differs based on the data scope.

Population Variance:

σ2 = Ni=1 xi μ2N

Sample Variance (Bessel's Correction):

s2 = ni=1 xi x2n 1

Reference Data

Sample Size (n)Correction Factor (nn 1)Bias Impact (%)
22.00050.0%
51.25020.0%
101.11110.0%
201.0535.0%
301.0343.3%
501.0202.0%
1001.0101.0%
5001.0020.2%
10001.0010.1%

Frequently Asked Questions

This is called Bessel's correction. When you calculate variance using a sample mean instead of the true population mean, the result is naturally biased downwards. Subtracting 1 from the sample size corrects this bias and provides an unbiased estimator of the population variance.
Use population variance when you have data for every single member of the group you are studying (e.g., the height of every student in a specific classroom). Use sample variance when your data represents a subset of a larger group (e.g., a survey of 100 voters representing a city).
Variance squares the difference between each point and the mean. This squaring process gives disproportionate weight to outliers. A single extreme value can drastically increase the variance result.