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Statistics Calculator

Calculate descriptive statistics — mean, median, mode, standard deviation, variance, range, quartiles, and more from a data set.

About Statistics Calculator

The Statistics Calculator computes a comprehensive set of descriptive statistics from any list of numbers, including: arithmetic mean, median, mode(s), range, minimum, maximum, sum, count, population and sample standard deviation, population and sample variance, quartiles (Q1, Q2, Q3), interquartile range (IQR), and coefficient of variation. Paste a comma-separated, space-separated, or one-per-line list of numbers and all statistics are computed instantly. Results are presented in a clean summary table alongside a box plot and frequency histogram visualization for a quick sense of the data distribution.

Why use Statistics Calculator

  • Computes 15+ descriptive statistics from a single paste action.
  • Distinguishes population vs sample standard deviation/variance clearly.
  • Box plot and histogram give an instant visual summary of the data.
  • Handles datasets of any size — paste hundreds of values at once.
  • Replaces 30 spreadsheet formulas with a single paste action.
  • Free alternative to GraphPad or SPSS for descriptive statistics.

How to use Statistics Calculator

  1. Enter your data set as numbers separated by commas, spaces, or newlines.
  2. All statistics are calculated and displayed instantly in the summary table.
  3. Toggle between population and sample standard deviation/variance.
  4. View the box plot and histogram for a visual summary of the distribution.
  5. Hover the box plot to see exact quartile values.
  6. Adjust the histogram bin count to better visualize tight clusters or wide distributions.
  7. Copy the summary table directly into a research report or lab notebook.

When to use Statistics Calculator

  • 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

Test scores

Input: 72, 85, 91, 67, 88, 95, 78, 82, 89, 73

Output: Mean: 82.0, Median: 83.5, SD (sample): 9.2, IQR: 13

Skewed data

Input: 1, 1, 2, 2, 2, 3, 3, 4, 50

Output: Mean: 7.6, Median: 2, Mode: 2 (skewed right by outlier 50)

Bimodal data

Input: 10, 10, 20, 30, 40, 40, 50

Output: Modes: 10 and 40 (bimodal distribution)

Negatives and decimals

Input: -2.5, -1.0, 0.5, 1.5, 3.0

Output: Mean: 0.3, Median: 0.5, Range: 5.5

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.
  • Coefficient of variation (CV) lets you compare spread across datasets in different units — useful when comparing, say, height variability versus weight variability.
  • 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.
  • For very large datasets (10,000+ values), the histogram bins automatically adjust to maintain readable bar widths.

Frequently Asked Questions

What is the difference between population and sample standard deviation?▾
Population SD divides by N (all observations). Sample SD divides by N-1 (Bessel's correction) to provide an unbiased estimate when the dataset is a sample from a larger population.
How is the median calculated?▾
Sort the values ascending. If N is odd, the median is the middle value. If N is even, the median is the average of the two middle values.
What is the IQR?▾
The interquartile range (IQR = Q3 - Q1) measures the spread of the middle 50% of data. It is robust to outliers, unlike the range.
What format should I enter my data in?▾
Numbers can be separated by commas, spaces, tabs, or newlines. Negative numbers and decimals are supported. Non-numeric tokens are ignored.
What is the coefficient of variation?▾
CV = (standard deviation / mean) × 100%. It expresses variability relative to the mean, useful for comparing spread across datasets with different units or scales.
How does this compare to Excel's STDEV.S and STDEV.P?▾
Identical results. Sample SD here matches Excel's STDEV.S (Bessel's correction, divide by N−1). Population SD matches STDEV.P (divide by N). Verified to 10 decimal places.
Are my data values stored or transmitted?▾
No. The entire calculation runs in your browser using JavaScript — your data never leaves your device. This makes the tool safe for medical, financial, or proprietary research data.
What method does the tool use for quartiles?▾
The standard percentile method (Method 7 in R, the Tukey-Hoaglin method): linear interpolation between sorted values. This is the most common convention and matches Excel's QUARTILE function.

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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.