Weighted Average Calculator

Enter up to eight values and give each one a weight. You get the weighted average — Σ(value × weight) ÷ Σ(weight) — plus the plain average, the total weight, and each value's contribution to the result.

Enter a value and a weight on each row. Leave unused rows blank — the weights don't have to add up to anything in particular.

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How the weighted average calculator works

A plain average pretends every number matters equally. Often that's a lie: a supplier rating where price matters more than delivery speed, an average share price across purchases of different sizes, survey results pooled from groups of different sizes. A weighted average fixes this by letting each value carry a weight — its share of the say in the final answer. Multiply each value by its weight, add everything up, and divide by the total weight.

This is the general-purpose version for any numbers. If what you're actually weighting is course grades against syllabus percentages, the weighted grade calculator is built for exactly that, with category names and a partial-semester mode.

The formula

Weighted average = Σ ( value × weight ) ÷ Σ weight

Each value is one of your numbers, each weight is how much that number should count, and Σ means "sum over every row you filled in." The weights don't need to sum to 100, to 1, or to anything else — the division by Σ weight normalizes them automatically, so 5-3-2 behaves identically to 50-30-20.

Worked example

You're scoring a supplier on three criteria: price 8/10 (weight 5, most important), reliability 6/10 (weight 3), and speed 9/10 (weight 2).

Weighted average = (8×5 + 6×3 + 9×2) ÷ (5 + 3 + 2) = (40 + 18 + 18) ÷ 10 = 7.6

The plain average of 8, 6, and 9 is about 7.67 — a touch higher, because it lets the strong speed score count as much as price. The weighted version correctly lets the mediocre reliability score, and the heavily weighted price, pull the result to 7.6.

When weighted beats plain — and when it doesn't

Use a weighted average whenever your numbers represent different amounts of stuff: different sample sizes, different dollar amounts, different importance. The classic failure is averaging averages — if one store's average sale is $50 across 1,000 sales and another's is $200 across 10 sales, the plain average of $125 is meaningless; weighting by sale count gives the true figure of about $51.49. On the flip side, don't invent weights you can't justify. If every observation genuinely counts the same, the plain average is the right tool, and adding weights just launders your gut feeling into a number that looks objective.

Frequently asked questions

How do I calculate a weighted average?

Multiply each value by its weight, add all those products up, then divide by the sum of the weights: weighted average = Σ(value × weight) ÷ Σ(weight). If every weight is the same, this collapses to the plain average — the weighted average is the general case.

Do the weights need to add up to 100 or 1?

No. The formula divides by whatever the weights total, so weights of 5-3-2 give exactly the same answer as 50-30-20 or 0.5-0.3-0.2. Use whatever numbers are natural for your data — sample sizes, dollar amounts, hours — and the math normalizes them for you.

What is the difference between a weighted average and a regular average?

A regular (plain) average treats every value as equally important: add them up and divide by the count. A weighted average lets some values count more than others. A plain average is really just a weighted average where every weight happens to be 1.

When should I use a weighted average instead of a plain average?

Whenever your data points don't deserve equal say: course grades where exams count more than homework, survey results combined across groups of different sizes, an average purchase price across orders of different quantities, or portfolio returns weighted by position size. If every observation genuinely counts the same, the plain average is correct.

Can a weight be zero or negative?

A weight of zero means the value contributes nothing — it's the same as leaving the row out, which is what this calculator does. Negative weights are technically possible in some advanced settings but almost always signal a modeling mistake, so this calculator skips those rows too.

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