Statistics
Statistics Calculator — Mean, Median, Mode & Range
Paste in a list of numbers and get a full statistical summary at once — mean, median, mode, range, count, sum, minimum, and maximum.
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📊 Enter Your Data Set
📊 Result
Enter a data set to see mean, median, mode, and more
Disclaimer: This tool performs standard mathematical calculations for educational and reference purposes. Always double-check results independently for graded coursework, exams, or safety-critical applications.
Understanding the Core Statistics
This calculator computes eight of the most commonly used descriptive statistics for a data set, each capturing a different aspect of the numbers — where they center, how spread out they are, and which values show up most often.
The Formulas
- Mean: sum of all values ÷ count of values
- Median: sort the data; if the count is odd, take the middle value; if even, average the two middle values
- Mode: the value(s) that occur most often — a data set can have one mode, several tied modes, or none if every value is unique
- Range: maximum value − minimum value
Worked Example
Data set: 2, 4, 4, 4, 5, 5, 7, 9 (8 values, sorted).
- Mean: (2+4+4+4+5+5+7+9) / 8 = 40 / 8 = 5
- Median: with 8 values, average the 4th and 5th sorted values: (4 + 5) / 2 = 4.5
- Mode: 4 appears three times, more than any other value, so the mode is 4
- Range: 9 − 2 = 7
Frequently Asked Questions
Median is more reliable than mean when a data set contains extreme outliers, because mean gets pulled toward very large or very small values while median only cares about the middle position. Household income and home prices, for example, are usually reported as medians for this reason.
Yes. If two or more values tie for the highest frequency, the data set is called multimodal and all of the tied values are reported as modes. If every value in the set appears exactly once, there's no meaningful mode at all, since nothing repeats more than anything else.
Range only looks at the two most extreme values — the maximum minus the minimum — and ignores everything in between. A single unusually high or low outlier can make the range look large even if the rest of the data is tightly clustered, which is why statisticians often prefer standard deviation for a fuller picture of spread.
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