Paste your data (comma or newline separated) to get the mean, median, mode, and range in one shot.
Mean is the arithmetic average — sum all values and divide by how many there are. Median is the middle value once the data is sorted (or the average of the two middle values, for an even count). Mode is whichever value appears most frequently. All three can differ substantially depending on how the data is shaped.
When your data has outliers or is heavily skewed. Mean gets pulled toward extreme values — a handful of very high incomes can drag a "mean income" figure well above what a typical person earns — while median stays anchored to the actual middle of the data regardless of how extreme the outliers are.
Yes. A bimodal data set has two values tied for the highest frequency; a multimodal set has more than two. If every value in the set appears exactly once, there's no meaningful mode at all, and this tool will show "No mode" in that case.
The Statistics Calculator also computes variance and standard deviation alongside central tendency. This tool is a simpler, faster version focused just on mean, median, mode, and range — useful when that's all you need without the extra output.
Worked example: for 3, 7, 7, 2, 9, 4, 7 (n=7): mean = 39/7 ≈ 5.57, sorted order is 2, 3, 4, 7, 7, 7, 9 so the median (4th of 7 values) is 7, the mode is 7 (appears three times), and the range is 9−2 = 7.
Take 1, 2, 2, 3, 100. The mean is 21.6, the median is 2, and the mode is 2. Four of the five values are below 3, yet the mean says 21.6 — because the mean gives every value equal weight, including one that is 30 times larger than the rest.
This is not a curiosity. It is why "average salary" and "median salary" tell different stories about the same workforce, and why the choice between them is sometimes a rhetorical one rather than a statistical one.
Comparing the mean and median is the fastest diagnostic you have. Mean noticeably above median means a right-skewed distribution with a long upper tail. Mean below median means left-skewed. Close together suggests roughly symmetric data, which is what most statistical methods assume.
Do that check before applying anything that assumes normality — including standard deviation and z-scores, both of which describe skewed data poorly.
A dataset can have no mode, or several. With an even number of values the median is the average of the two middle ones, which means it may not be a value that actually appears in your data. Neither is a problem — but both surprise people who expect a single tidy answer.
Range, reported alongside these, is the crudest measure of spread: it uses only two values and ignores everything between them. Two datasets with identical ranges can be shaped completely differently.