Statistics

F Test Variance Calculator

Compare sample variances with F statistic and degrees of freedom.

Last validated: 2026-02-14

F Test Variance Calculator compares variances of two samples to test whether spread differs significantly. It is useful when checking homogeneity assumptions before other statistical tests. The tool computes F statistic and significance from sample variance inputs. Use it as part of variance analysis workflows.

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Input Pattern

Enter values in the left panel, keep units explicit, run the calculation, then copy or share the result. Invalid fields are highlighted immediately.

F-test Samples

Enter numeric lists for both samples.

Result

Variance A: 2.00000000

Variance B: 2.00000000

F statistic (>=1): 1.00000000

DF1/DF2: 5 / 5

How to use this tool

Enter sample sizes and variances for both groups.
Run the F-test to compute test statistic and p-value.
Use results to assess whether variance difference is statistically meaningful.

Worked Example

Auto-generated from the tool's current default or entered inputs.

Example Inputs

Sample a: 12,14,13,15,16,14
Sample b: 8,9,11,10,7,9
Var a: 2.0
Var b: 2.0
F stat: 1.0
Df1: 5
Df2: 5

Expected Outputs

Parsed a 0: 12
Parsed a 1: 14
Parsed a 2: 13
Parsed a 3: 15

Interpretation

Parsed a 0 is 12.0. Use this as a decision input, not as a standalone conclusion.
Parsed a 1 is 14.0. Use this as a decision input, not as a standalone conclusion.
Parsed a 2 is 13.0. Use this as a decision input, not as a standalone conclusion.
Interpret this value alongside sample quality and assumptions to avoid overconfidence.

Scenario Compare (A vs B)

Use this to compare two input sets and quantify change in key outputs.

Scenario A

Sample a

Sample b

Var a

Var b

F stat

Df1

Df2

Scenario B

Sample a

Sample b

Var a

Var b

F stat

Df1

Df2

Confidence and limitations

Outputs are deterministic formula results and depend entirely on input quality and units.
Results are for planning and learning; verify with domain standards before safety-critical use.
Interpretation depends on assumptions (distribution, independence, sample quality).

Formula References

Frequentist estimation and hypothesis-test formulas used in standard textbooks.
Interpretation depends on sample quality and assumptions.

Assumptions

Outputs are deterministic formula results and depend entirely on input quality and units.
Results are for planning and learning; verify with domain standards before safety-critical use.
Interpretation depends on assumptions (distribution, independence, sample quality).

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