STATISTICSDescriptive StatisticsStatistics Calculator
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Variance Calculator

Free variance calculator. Compute sample variance, population variance, grouped data variance, weigh

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Why This Statistical Analysis Matters

Why: Statistical calculator for analysis.

How: Enter inputs and compute results.

σ²
STATISTICSDescriptive Statistics

Variance Calculator — Sample & Population with 5 Input Modes

Raw data, frequency table, grouped data, weighted data, or from summary. Step-by-step computation, deviation squares visualization, and distribution charts.

Real-World Scenarios — Click to Load

Data Input

variance_results.sh
CALCULATED
$ variance --type="sample" --mode="raw"
Count (n)
10
Mean (μ)
84.0000
Variance (s²)
54.6667
Std Dev (s)
7.3937
Sum of Squared Deviations
492.0000
Coefficient of Variation
8.80%
Share:
Variance Result
Sample s²
54.6667
n = 10μ = 84.00SD = 7.39CV = 8.8%
numbervibe.com/calculators/statistics/variance-calculator

Data Distribution with Variance Band (μ ± σ)

Deviation Squares Visualization

Each bar shows (xᵢ − μ)². Sum of all bars = numerator in variance formula.

Population vs Sample Variance Comparison

Step-by-Step Table: xᵢ, x̄, (xᵢ−x̄), (xᵢ−x̄)²

ixᵢ(xᵢ − x̄)(xᵢ − x̄)²
172.000084.0000-12.0000144.0000
285.000084.00001.00001.0000
390.000084.00006.000036.0000
478.000084.0000-6.000036.0000
592.000084.00008.000064.0000
688.000084.00004.000016.0000
776.000084.0000-8.000064.0000
881.000084.0000-3.00009.0000
995.000084.000011.0000121.0000
1083.000084.0000-1.00001.0000

Calculation Breakdown

MEAN
Step 1: Compute mean (μ)
μ = Σxᵢ/N = 84.0000
μ = Σxᵢ/10
DEVIATIONS
Step 2: Deviations (xᵢ − μ)
Each value minus mean
SQUARED
Step 3: Square deviations (xᵢ − μ)²
Sum = 492.0000
VARIANCE
Step 4: Divide by n−1
492.0000 / 9 = 54.6667
Variance (s²)
54.6667
Standard Deviation (s)
√54.6667 = 7.3937
Coefficient of Variation
CV = 7.3937/84 × 100 = 8.80%
SHORTCUT
Shortcut: s² = [Σx² − (Σx)²/n] / (n−1)
54.6667

For educational and informational purposes only. Verify with a qualified professional.

Key Takeaways

  • Population variance (σ²) divides by N — use when you have data for every member of the group.
  • Sample variance (s²) divides by n−1 (Bessel's correction) — use when your data is a subset. Produces an unbiased estimate.
  • Shortcut formula s² = [Σx² − (Σx)²/n] / (n−1) is computationally efficient for raw data.
  • Grouped data uses class midpoints and frequencies: s² = [Σfᵢmᵢ² − (Σfᵢmᵢ)²/Σfᵢ] / (Σfᵢ − 1).
  • Weighted variance applies when observations have different importance (weights).
  • • Standard deviation (σ or s) = √variance. CV = SD/mean × 100% for relative comparison.

Why Use n−1 for Samples? (Bessel's Correction)

When you divide by n instead of n−1 for sample variance, the result systematically underestimates the true population variance. Dividing by n−1 corrects this bias.

Intuition: The sample mean x̄ is always closer to the sample data than the true population mean μ. So deviations from x̄ are smaller on average than deviations from μ, producing a biased (low) estimate. The n−1 denominator restores the degrees of freedom lost when estimating μ from the sample.

Variance Properties

Scaling: Var(aX + b) = a²Var(X) — variance is unaffected by adding a constant, but scaling by a multiplies variance by a².

Independence: Var(X + Y) = Var(X) + Var(Y) when X and Y are independent. For dependent variables, add the covariance term.

Non-negativity: Variance is always ≥ 0. Zero variance means all values are identical.

Step-by-Step Computation Table

For raw data, build a table: xᵢ | x̄ | (xᵢ − x̄) | (xᵢ − x̄)². Sum the squared deviations, then divide by n−1 (sample) or N (population).

Example: Data 2, 4, 6, 8, 10 → Mean = 6

Deviations: -4, -2, 0, 2, 4 → Squared: 16, 4, 0, 4, 16 → Sum = 40

Population σ² = 40/5 = 8. Sample s² = 40/4 = 10.

Grouped Data Variance

When data is in class intervals with frequencies, use midpoints mᵢ = (lower + upper)/2. Formula: s² = [Σfᵢmᵢ² − (Σfᵢmᵢ)²/Σfᵢ] / (Σfᵢ − 1). Enter as "lower-upper:frequency" per line.

Weighted Variance

When observations have different weights wᵢ, the weighted mean is x̄_w = Σwᵢxᵢ / Σwᵢ. Weighted sample variance: s²_w = Σwᵢ(xᵢ − x̄_w)² / (Σwᵢ − 1). Enter as value:weight per line.

From Summary Statistics

If you have Sum of Squares (SS) and n, variance = SS / (n−1) for sample or SS / N for population. If you have SD, variance = SD². Use the "From summary" mode.

Frequently Asked Questions

When should I use population vs sample variance?

Use population variance when your dataset includes every member of the group. Use sample variance (n−1) when your data is a subset — this is the default in most research and software.

Can variance be negative?

No. Variance is a sum of squared deviations, so it is always non-negative. Zero variance means all values are identical.

What is the difference between variance and standard deviation?

Variance is the average squared deviation; standard deviation is its square root. SD has the same units as your data, making it easier to interpret.

How do outliers affect variance?

Variance is highly sensitive to outliers because deviations are squared. A single extreme value can dramatically increase the variance.

What is the coefficient of variation?

CV = (SD / |mean|) × 100%. It allows comparison of variability across datasets with different units or scales.

Pooled Variance (Two or More Groups)

When combining variances from k groups: s²_p = Σ(nᵢ−1)sᵢ² / Σ(nᵢ−1). Used in two-sample t-tests and ANOVA. For a single dataset, use the modes above.

Disclaimer: This calculator provides accurate variance computations using standard formulas. For critical applications, verify results. Grouped data assumes midpoints represent class values.

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