Find the mean, median, mode, range, and standard deviation of any list of numbers with this average calculator.
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Enter numbers in the inputs on the left to calculate mean, median, mode, and standard deviation instantly.
The arithmetic mean formula calculates the central average value of a sample dataset by summing all observation values and dividing by the total sample count.
The arithmetic mean is the sum of all values divided by the count: Mean = (Σx) ÷ n. The median is the middle value after sorting the dataset in ascending order (or the average of the two middle values when n is even), and the mode is the value that occurs most frequently.
A weighted mean multiplies each value by its assigned weight before dividing by the total weight, which differs from a plain average and matters for weighted course grades or portfolio returns. A common mistake is confusing sample standard deviation, which divides squared differences by (n − 1), with population standard deviation, which divides by n; the calculator reports both separately.
For the dataset 10, 20, 30, 40, 50: the sum is 150, so the mean is 150 ÷ 5 = 30. The sorted list has 30 as the exact middle value, so the median is also 30, while the range (max − min) is 50 − 10 = 40.
Use the Average Calculator for exam scores, sales figures, or survey data when you need mean, median, mode, and dispersion in one view. Pair it with the Fraction Calculator when working with fractional weights, or the LCM & HCF Calculator when simplifying ratios within a dataset.
Quick answers about how this tool works.
For an even set of numbers, sort the values in ascending order and calculate the mean of the two middle numbers.
A weighted average multiplies each number by an assigned weight before dividing by the sum of all weights, useful for weighted course grades or stock portfolios.
Yes. If no value repeats, there is no mode. If multiple values tie for highest frequency, the dataset is bimodal or multimodal.
Standard deviation measures data dispersion around the mean. Sample variance divides sum of squared differences by (N - 1), while population variance divides by N.
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