Statistics

ANOVA One Way Calculator

Run one-way ANOVA summary statistics for multiple groups.

Last validated: 2026-02-14

ANOVA One Way Calculator compares means across multiple groups to test whether at least one differs significantly. It is useful for experiment analysis and comparing variants in controlled studies. The tool computes ANOVA summary metrics from grouped samples. Use it as a first pass before post-hoc testing.

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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.

One-way ANOVA Input

Use semicolons between groups, commas/spaces within group.

Result

F statistic: 7.92307692

DF between/within: 2 / 6

MS between: 11.444444

MS within: 1.444444

Interpret with an F table or statistical package for p-values.

How to use this tool

Enter sample values for each group.
Run ANOVA to compute between/within variance and test statistic.
Use p-value result to decide whether group means differ significantly.

Worked Example

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

Example Inputs

Groups: 12,14,15; 9,11,10; 13,14,12
Group count: 3
Ss between: 22.888888888888882
Ss within: 8.666666666666668
Df between: 2
Df within: 6
Ms between: 11.444444444444441
Ms within: 1.4444444444444446

Expected Outputs

Parsed groups 0 0: 12
Parsed groups 0 1: 14
Parsed groups 0 2: 15
Parsed groups 1 0: 9

Interpretation

Parsed groups 0 0 is 12.0. Use this as a decision input, not as a standalone conclusion.
Parsed groups 0 1 is 14.0. Use this as a decision input, not as a standalone conclusion.
Parsed groups 0 2 is 15.0. 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

Groups

Group count

Ss between

Ss within

Df between

Df within

Ms between

Ms within

Scenario B

Groups

Group count

Ss between

Ss within

Df between

Df within

Ms between

Ms within

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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