Statistics Calculator
Calculate mean, median, mode, standard deviation, variance, quartiles, and more from any set of numbers. Paste your data and get results instantly.
About the Statistics Calculator
Descriptive statistics summarise the key properties of a dataset in a small set of numbers — the average, the spread, the extremes, and the shape of the distribution. Rather than presenting a list of 100 raw values, a statistician reports the mean, standard deviation, and a few other measures that capture the essential character of the data. These summaries are the foundation of virtually every quantitative field: from academic research and clinical trials to business analytics, quality control, and data science.
Our statistics calculator computes thirteen core metrics from any list of numbers you paste or type. Separate values by commas or line breaks, click calculate, and instantly see the count, sum, mean, median, mode(s), minimum, maximum, range, variance, standard deviation, and the three quartile values (Q1, Q3, and the interquartile range). The sorted dataset is displayed alongside results so you can visually verify the output.
Understanding when to use each statistic matters as much as calculating it correctly. The mean is sensitive to outliers — a single extreme value can pull it far from the typical observation. The median, being the middle value of the sorted dataset, is resistant to outliers and gives a better picture of 'typical' in skewed distributions like income or house prices. The standard deviation quantifies spread around the mean, and the IQR quantifies spread around the median. Together, these statistics give a complete picture of any numeric distribution.
Pros & Cons
- +Computes 13 statistics simultaneously from any numeric dataset
- +Accepts comma- or newline-separated inputs for easy paste-in from spreadsheets
- +Handles any sample size from 2 to thousands of values
- +Shows sorted dataset for visual verification
- +Covers both population and sample standard deviation variants
- −Descriptive statistics only — does not perform hypothesis tests or regression
- −Does not produce visualisations (histogram, box plot) natively
- −No support for categorical or text data
- −Large datasets may require careful formatting before pasting
- −Does not handle missing values — all inputs must be valid numbers
Understanding the Core Statistics
The mean (arithmetic average) is calculated by summing all values and dividing by the count. It is the most widely used measure of central tendency but is vulnerable to outliers. If nine people earn $50,000 and one earns $1,000,000, the mean salary is $145,000 — a figure that accurately describes no one in the group. The median — the middle value when the data is sorted — would be $50,000, a far more representative description of a 'typical' salary.
Standard deviation measures how spread out values are around the mean. A small standard deviation means values cluster tightly around the mean; a large one means they are widely dispersed. The formula for sample standard deviation (the default for most real-world datasets, which are samples from a larger population) is: s = √[Σ(xᵢ − x̄)² / (n − 1)], where the division by (n − 1) rather than n is called Bessel's correction and removes bias in the sample estimate.
Quartiles divide the sorted dataset into four equal parts. Q1 (the first quartile) is the median of the lower half of the data; Q3 is the median of the upper half. The interquartile range (IQR = Q3 − Q1) is the range of the middle 50% of values and is a robust measure of spread that ignores extreme outliers. Values more than 1.5 × IQR below Q1 or above Q3 are conventionally flagged as outliers in box-plot analysis.