Statistics Calculator

About this tool

The Statistics Calculator computes a set of descriptive statistics from any list of numbers: count, sum, arithmetic mean, median, mode(s), range, minimum and maximum, population and sample variance, population and sample standard deviation, the first and third quartiles (Q1, Q3) and the interquartile range (IQR). Paste a comma-separated, space-separated, or one-per-line list of numbers; results update as you type, and the Calculate button reruns them. Results are grouped into three blocks — central tendency, spread, and extremes & quartiles — so the population (divide by N) and sample (divide by N−1) figures sit side by side. Quartiles are read directly from the sorted data at positions n/4 and 3n/4 (rounded down), without interpolation, so they can differ slightly from spreadsheet QUARTILE functions on small datasets.

Why use it

• Computes 15 descriptive statistics from a single paste.

• Shows population and sample standard deviation/variance side by side, so you can't pick the wrong one by accident.

• Reports every mode for multimodal data, or 'No mode' when all values are distinct.

• Handles datasets of any size — paste hundreds of values at once.

• Replaces a dozen spreadsheet formulas with a single paste action.

• Free alternative to a stats package for quick descriptive statistics.

How to use

1. Enter your data set as numbers separated by commas, spaces, semicolons, or newlines.
2. Results update as you type; you can also press Calculate.
3. Read count, sum, mean, median, mode and range in the first block.
4. Use the Spread block to compare population variance and σ with sample variance (s²) and s.
5. Check minimum, Q1, median, Q3, maximum and IQR in the Extremes & Quartiles block.
6. Copy the figures you need into your report or lab notebook by hand.

When it helps

• Summarizing a dataset in statistics coursework or research.
• Checking descriptive statistics before performing inferential analysis.
• Quickly finding mean, median, and SD from exported spreadsheet data.
• Exploring data distributions before choosing a statistical test.
• Reporting summary statistics for a research paper methods section.
• Quick ad-hoc data exploration before opening a heavyweight stats package.

Examples

Tips

• Use sample standard deviation (divide by N−1) when your data is a sample from a larger population — this is the right default in most research and engineering contexts.
• Watch the relationship between mean and median: a large gap signals a skewed distribution and possible outliers.
• The IQR (Q3 − Q1) is your best friend for outlier detection — points outside Q1 − 1.5×IQR or Q3 + 1.5×IQR are typical outliers.
• To compare spread across datasets in different units, divide the standard deviation by the mean yourself to get the coefficient of variation.
• If your dataset has multiple modes, the tool reports all of them (multimodal data is common in real-world surveys).
• Paste tab-separated data directly from a spreadsheet column — the parser handles tabs, commas, spaces, and newlines.
• If you need quartiles that match a specific textbook or Excel method, check them against that method — this tool uses a simple non-interpolated position rule.

Frequently Asked Questions

Glossary

Mean (arithmetic average)

The sum of values divided by their count. Sensitive to outliers — a few extreme values can pull the mean far from the typical observation.

Median

The middle value when the data is sorted. Robust to outliers — useful when data is skewed (e.g., income, house prices).

Mode

The most frequently occurring value(s). A dataset can be unimodal, bimodal, or multimodal.

Standard deviation

A measure of dispersion equal to the square root of the variance. Roughly 68% of normally distributed data lies within ±1 SD of the mean.

Variance

The average squared deviation from the mean. Has squared units, which is why we usually report standard deviation.

Quartile

Values that split sorted data into four equal parts. Q1 (25th percentile), Q2 (50th = median), Q3 (75th percentile).

Interquartile range (IQR)

IQR = Q3 − Q1. Measures the spread of the middle 50% of data — a robust alternative to standard deviation.

Coefficient of variation (CV)

CV = (SD / Mean) × 100%. A unitless measure of relative variability that lets you compare datasets in different units.

Bessel's correction

Dividing by N−1 instead of N when computing sample variance. This corrects for bias when estimating a population parameter from a sample.