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Statistics Calculator

Paste numbers separated by commas, spaces, tabs or new lines — a column copied straight from a spreadsheet works as it is — and every descriptive statistic is worked out at once. Both the sample and the population forms of variance and standard deviation are shown side by side rather than one being silently chosen for you, because which is correct depends on whether your numbers are everything or a subset of something larger. Quartiles use linear interpolation, the same method as NumPy and Excel’s PERCENTILE.INC, so the numbers match what you would get there. Outliers are identified by the 1.5 × IQR rule that a box plot’s whiskers use, and a distribution strip shows the shape of the data at a glance.

How it works

Paste your numbers separated by commas, spaces, tabs or new lines — a column copied straight out of a spreadsheet works as it is, and anything that is not a number is ignored rather than breaking the calculation.

Both the sample and population forms of variance and standard deviation are shown: use the sample row when your numbers are a subset of something larger, the population row when they are everything.

10 values read
Try:

Central tendency

Where the middle of the data sits.

Count
10
Sum
796
Mean (average)
79.6
Median
82
Middle value once sorted
Mode
85
appears 3 times
Midrange
77
Geometric mean
78.9321
the nth root of the product
Harmonic mean
78.2301
for rates and ratios
Root mean square
80.2309

Spread

How far the values sit from the middle. Use the sample row unless your data is the entire population.

Sample std. deviation (s)
10.5851
divides by n − 1
Population std. deviation (σ)
10.0419
divides by n
Sample variance (s²)
112.0444
Population variance (σ²)
100.84
Standard error
3.3473
s ÷ √n
Coefficient of variation
13.2979%
s as a share of the mean
Mean absolute deviation
8.4
Median absolute deviation
7
Sum of squares
1008.4
Σ(x − mean)²

Range and quartiles

Outliers are values beyond 1.5 × IQR from the nearest quartile — the same rule a box plot's whiskers use.

Minimum
60
Q1 (25th percentile)
73.25
Q2 (median)
82
Q3 (75th percentile)
85
Maximum
94
Range
34
Interquartile range
11.75
Lower fence
55.625
Upper fence
102.625
Outliers
none

Shape

Skewness says which tail is longer; excess kurtosis compares the tails with a normal distribution, where it is 0.

Skewness
-0.5395
negative — a longer tail to the left
Excess kurtosis
-0.3145
lighter tails than a normal distribution

Distribution

5 equal-width bins across the full range.

6066.8
1
66.873.6
2
73.680.4
2
80.487.2
3
87.294
2

Sorted values

60, 68, 72, 77, 79, 85, 85, 85, 91, 94

Frequently asked questions

What is the difference between sample and population standard deviation?

The sample form divides by n − 1 and is correct when your numbers are a subset of a larger group; the population form divides by n and is correct when they are the entire group. Both are shown so you can pick the right one.

How are the quartiles calculated?

By linear interpolation between the surrounding values — the same method NumPy uses by default and Excel’s PERCENTILE.INC — so the results match those tools rather than differing by a fraction.

How does it decide what counts as an outlier?

Any value more than 1.5 × the interquartile range below Q1 or above Q3. That is Tukey’s rule, and it is what a box plot’s whiskers mark.

What format should I paste my numbers in?

Any — commas, spaces, tabs or one per line all work, and anything that is not a number is skipped and reported rather than breaking the calculation.

Why does it say there is no mode?

Because no value repeats. When every value appears once, calling them all modes tells you nothing, so no mode is reported instead. If several values tie for the highest count, all of them are listed.

Are my numbers uploaded anywhere?

No. Everything is calculated in your browser, so figures from a real dataset stay on your device.

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