Math

Weighted Average Calculator

Compute weighted average from value and weight lists.

Last validated: 2026-02-14

Weighted Average Calculator computes averages where each value contributes by assigned weight. It is useful for grading, portfolio metrics, and composite scoring models. The tool helps avoid incorrect simple-average substitutions. Use it when contribution weights are unequal.

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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.

Weighted Average Inputs

Enter comma-separated values and matching weights.

Result

Weighted average: 84.000000

Weight sum: 1.000000

How to use this tool

Enter values and corresponding weights.
Run calculation to produce weighted average.
Check that weights reflect your intended contribution model.

Worked Example

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

Example Inputs

Values: 80, 90, 75
Weights: 0.3, 0.5, 0.2
Weighted average: 84.0
Same length: true

Expected Outputs

Parsed values 0: 80
Parsed values 1: 90
Parsed values 2: 75
Parsed weights 0: 0.3

Interpretation

Parsed values 0 is 80.0. Use this as a decision input, not as a standalone conclusion.
Parsed values 1 is 90.0. Use this as a decision input, not as a standalone conclusion.
Parsed values 2 is 75.0. 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.

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.
Rounding and finite precision can introduce small differences in edge cases.

Formula References

Algebra and arithmetic identities used by the selected formula.
Standard precision arithmetic; rounding shown for readability.

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.
Rounding and finite precision can introduce small differences in edge cases.

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