Calculate mean, median, mode, standard deviation and more
Built & maintained by Pappu Venkata Subbi Reddy, founder of Clacify · Updated August 2026
The Statistics Calculator turns a list of numbers — pasted or typed, separated by commas, spaces, or new lines — into a full descriptive-statistics summary: count, sum, mean, median, mode, minimum, maximum, range, first and third quartile (Q1/Q3), variance, and standard deviation. It's built for students checking homework, anyone summarising a small dataset (test scores, survey responses, measurements), or someone who just wants the numbers without opening a spreadsheet. Every figure updates live as you edit the input, and the twelve results are laid out as a clean, copyable reference table.
Your input is split on commas, whitespace, and line breaks, converted to numbers, and any value that fails to parse is dropped. Mean is the sum divided by count. Median sorts the values and takes the middle one (or averages the two middle values for an even count). Mode is found by frequency count, returning every value tied for the highest frequency. Q1 and Q3 are read directly off the sorted array at the 25th and 75th percentile positions — a straightforward positional method, not the linear-interpolation method some statistics packages (like Excel's PERCENTILE.INC) use, so values may differ slightly from those tools on small datasets. Variance and standard deviation are calculated as the population versions — variance = the average of each value's squared distance from the mean, divided by n (the full count), and standard deviation is its square root. This is the right choice when your numbers are the entire dataset you care about; if your numbers are a sample meant to estimate a larger population's spread, the standard statistical convention is to divide by n−1 instead (Bessel's correction), which this calculator does not apply — for small samples the two can differ noticeably, so keep that distinction in mind if you're using the result for inferential statistics rather than describing the data you have.
Statistics has two versions of variance for a reason: population variance (divide by n) describes the spread of a complete dataset exactly as it is, while sample variance (divide by n−1) is a correction used when your numbers are a sample drawn from a bigger population, because dividing by n on a sample systematically underestimates the true population variance. This calculator always uses the population formula (÷n). For a full dataset — every student's score, every day's reading — that's the mathematically correct choice. If you're instead using a handful of measurements to estimate a wider population's variability (a common case in research and quality control), calculate variance separately with n−1 in the denominator rather than reading it off this tool.
For the numbers 12, 15, 18, 22, 25, 18, 30, 18, 10 (nine values): the mean is 168 ÷ 9 ≈ 18.67, the sorted list is 10, 12, 15, 18, 18, 18, 22, 25, 30 so the median (the 5th value) is 18, and 18 is also the mode since it appears three times — more than any other value. The range is 30 − 10 = 20. This is the exact default example pre-loaded in the tool, so you can compare your own results against it to confirm you're reading the output correctly.
There isn't one universally agreed way to compute Q1 and Q3 — statistics textbooks and software packages use several slightly different conventions, and they can disagree by a fraction of a unit on the same dataset. This tool takes the value at the sorted position ⌊n/4⌋ for Q1 and ⌊3n/4⌋ for Q3 (0-indexed), which is simple and consistent but won't always match a linear-interpolation method like Excel's QUARTILE/PERCENTILE functions exactly, especially on small datasets. For rough analysis and coursework this rarely matters; for precise inferential work, confirm which convention your course or tool expects.
Mean is the arithmetic average (sum ÷ count). Median is the middle value when sorted (or average of two middle values for even count). Mode is the value that appears most frequently. For normally distributed data, mean ≈ median ≈ mode. For skewed data (like income), median is a better "typical value" than mean because it is not distorted by extreme outliers.
Standard deviation measures how spread out values are from the mean. A low standard deviation means values cluster close to the mean; a high value means they are widely spread. Formula: σ = √[Σ(xi − μ)² ÷ N]. In a normal distribution, 68% of values fall within 1 standard deviation of the mean, and 95% within 2 standard deviations.