Mean Calculator
Enter a list of numbers to get the arithmetic, geometric, harmonic, and quadratic mean all at once.
μ What is a Mean?
A mean is a single value that represents the center or typical value of a dataset. While most people are familiar with the everyday arithmetic mean, mathematics defines four classical means: the arithmetic mean (AM), geometric mean (GM), harmonic mean (HM), and quadratic mean (QM, also called root mean square or RMS). Each type of mean is the correct choice for a different kind of data.
The arithmetic mean is the one you use most often: add all values and divide by the count. It works best for symmetric data like test scores, temperatures, and ages where values cluster around a center. The geometric mean is the correct average for growth rates and ratios because it reflects multiplicative relationships. If an investment grows 100% one year and falls 50% the next, the arithmetic mean return is 25%, but the true annualized return is 0%, which only the geometric mean captures. The harmonic mean is ideal for averaging rates, speeds, and unit prices where the denominator (distance, time, quantity) is fixed. The quadratic mean (RMS) is used in physics, engineering, and statistics to measure the magnitude of varying quantities, especially when values can be positive or negative.
A key mathematical relationship links the three positive-value means: the arithmetic mean is always greater than or equal to the geometric mean, which is always greater than or equal to the harmonic mean. This is called the AM-GM-HM inequality and it holds for any set of positive numbers. All three means are equal only when every value in the dataset is identical. The quadratic mean is always at least as large as the arithmetic mean.
This calculator computes all four classical means simultaneously from a single dataset, so you can instantly compare them. The Weighted Mean mode lets you assign different importance to each value, which is essential for grade point averages, index weighting, and survey analysis where not all observations carry equal significance.