Variance Calculator
Calculate population and sample variance to measure data variability. Get detailed statistical analysis with visual representations and step-by-step calculations.
Variance Calculation
Population Variance
Sample Variance
Population Variance (σ²)
Used when you have data for the entire population. Divides by N (total count).
Results
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Population Variance (σ²)
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Standard Deviation (σ)
Variance & Standard Deviation Relationship
Standard Deviation = √Variance
σ = √σ² s = √s²
σ = √σ² s = √s²
How to Use the Variance Calculator
Variance measures how much individual data points deviate from the mean, providing insight into data spread and consistency. Our calculator supports both population variance (when you have complete data for an entire group) and sample variance (when working with a subset that represents a larger population). Population variance divides the sum of squared deviations by N (total observations), while sample variance uses N-1 (Bessel's correction) to provide an unbiased estimate. Simply enter your data values, select the appropriate variance type, and get comprehensive results including standard deviation, mean, and visual representations of your data distribution.
Population Variance:
σ² = Σ(x - μ)² / N
Sample Variance:
s² = Σ(x - x̄)² / (n - 1)
σ² = Σ(x - μ)² / N
Sample Variance:
s² = Σ(x - x̄)² / (n - 1)
📊 Quality Control Example:
Scenario: Manufacturing process producing widgets with target weight of 100g
Sample weights (grams): 98, 102, 99, 101, 97, 103, 100, 99
Sample mean: x̄ = 99.875g
Squared deviations: (98-99.875)², (102-99.875)², ... = 26.875
Sample variance: s² = 26.875 ÷ 7 = 3.839
Standard deviation: s = √3.839 = 1.96g
Interpretation: Low variance indicates consistent manufacturing process
Understanding variance is crucial for quality control, risk assessment, portfolio management, and scientific research. A higher variance indicates greater variability and potential inconsistency, while lower variance suggests more predictable, stable outcomes. In finance, variance helps measure investment risk; in manufacturing, it indicates process control; in research, it shows data reliability. The relationship between variance and standard deviation is fundamental - standard deviation is simply the square root of variance, making it easier to interpret since it's in the same units as the original data.