Geometric Mean Calculator
Find the geometric mean of any data set.
Result
How it is calculated
The geometric mean of n positive numbers is the nth root of their product: GM = (x₁ × x₂ × … × xₙ)^(1/n). The tool computes it through logarithms, GM = exp(average of ln xᵢ), which gives the same value and avoids overflow with long lists. Numbers can be separated by commas, semicolons or spaces. Items that are not numbers are ignored, and an empty list gives no result. Every value must be greater than zero; a zero or negative value makes the geometric mean undefined and the tool returns nothing. Use it for growth factors and ratios, where the arithmetic mean overstates the typical value.
Worked example
The numbers 2, 8 and 4. Their product is 2 × 8 × 4 = 64, and there are 3 values, so the geometric mean is the cube root of 64, which is 4.00. The arithmetic mean of the same numbers is 4.67, noticeably higher.
Common mistakes
- Including a zero or a negative number: the geometric mean is undefined, so the tool returns nothing.
- Averaging percentage returns directly; convert them to growth factors (+10% becomes 1.10) first and subtract 1 at the end.
- Using the arithmetic mean for growth rates, which overstates the average yearly growth.
- Mixing a decimal comma with the list separator: write decimals with a point and separate items with spaces or semicolons.
- Typing a stray word or symbol and assuming it counted: non-numeric items are silently skipped.
About this calculator
This calculator finds the nth root of the product of a set of numbers, rather than their sum divided by count (the arithmetic mean). This makes it the correct way to average rates of change or growth factors, since it doesn't get distorted the way an arithmetic mean does by one unusually large or small value.
It's used by finance analysts computing an investment's true annualized return across several years (the basis of CAGR), ecologists averaging population growth rates across seasons, and data teams aggregating ranking or index scores where multiplicative relationships matter more than additive ones.
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