Average Calculator

The arithmetic mean is the most familiar 'average': add every value and divide by the count. It is the right measure when your data is roughly symmetric and free of extreme outliers. The median is the middle value once the list is sorted (or the average of the two middle values for even-length lists). The mode is the value that appears most often.

Choosing the wrong one is the most common stats mistake in news reporting. US household income is a textbook example: the mean is pulled upward by a small number of very high earners, so it always exceeds the median by a wide margin. The Census Bureau publishes the median ($80,610 in 2023) precisely because it represents the 'typical' household better than the mean ($114,500) would.

Suppose your team's weekly sales (in thousands) are: 18, 22, 19, 24, 19, 21, 95. Count = 7. Sum = 218. Mean = 218 / 7 ≈ 31.1. Sorted, the list is 18, 19, 19, 21, 22, 24, 95 — median = 21 (the middle value). Mode = 19 (it appears twice). Min = 18, Max = 95, Range = 77.

The mean of 31.1 misleads anyone glancing at the table because it suggests an average week pulls in $31k. Six of the seven weeks are below that. The 95 is almost certainly an outlier — a one-off enterprise deal or a data-entry error. Reporting both the mean and the median, with the range to flag the spread, gives a reader the full picture in one line.

If you suspect outliers are systematic rather than rare, use a trimmed mean (drop the top and bottom 10%) or compute the median directly. This calculator surfaces both so you do not have to choose blind.

Range (max − min) is the simplest spread measure but is dominated by outliers. Two datasets can have the same range and very different shapes.

Standard deviation and variance describe how tightly the data cluster around the mean. For those, use our Standard Deviation Calculator, which also handles sample vs population denominators correctly.

Quartiles and the interquartile range (IQR) are robust to outliers — Q1 is the 25th percentile, Q3 the 75th, and IQR = Q3 − Q1 captures the middle half of your data. Datasets with the same mean and standard deviation but different IQRs often have very different practical meaning.

If your numbers represent rates, percentages, or anything multiplicative (investment returns, population growth), use the geometric mean instead of the arithmetic mean. The geometric mean of 1.10 and 0.90 is √(1.10 × 0.90) ≈ 0.995, not the arithmetic mean of 1.00.