In short: For a list of numbers, the mean is the sum divided by the count, the median is the middle value when sorted, the mode is the most frequent value, and the standard deviation measures how spread out the values are.
About Mean, Median, Mode & Standard Deviation
Summary statistics describe a data set in a few numbers. The mean (average) is sensitive to extreme values, so the median — the middle value — is often a better “typical” figure for things like incomes or house prices. The mode tells you the most common value, and the range, quartiles and standard deviation describe how spread out the data is.
Standard deviation is shown two ways. The sample standard deviation (dividing by n − 1) is the right one when your numbers are a sample from a bigger population — for example survey responses. The population version (dividing by n) applies when you have every value. Quartiles use the same method as Excel’s QUARTILE.INC.
How to use Mean, Median, Mode & Standard Deviation
- Paste or type your numbers, separated by commas, spaces, tabs or line breaks (copying a spreadsheet column works).
- Read the statistics, which update as you type.
- Check the sorted list underneath for typos or outliers.
Examples
12, 15, 11, 18, 15, 20, 14, 15, 9, 17 → mean 14.6, median 15, mode 15, sample SD ≈ 3.31.
Salaries 40k, 42k, 45k, 47k, 250k: mean 84.8k but median 45k — the median better reflects a typical salary.
Frequently asked questions
Should I use sample or population standard deviation?
Use sample (n − 1) unless your data includes every member of the group you are describing.
What if there is no mode?
If every value appears once, there is no mode. If several values tie for most frequent, all of them are listed.
Which quartile method is used?
The inclusive method (like Excel QUARTILE.INC and NumPy’s default linear interpolation).
Can I paste numbers with thousands separators?
Use plain numbers like 1500 or 1500.5. Commas are treated as separators between values.
Updated November 2026 · Free, no signup · How we build and test tools