Every statistic your data holds, in one place.
Paste any set of numbers and see the whole picture: the center, the spread and the shape. Mean, median, mode, standard deviation, quartiles, skewness and kurtosis, all computed live as you type.
Your data set
8 values read.
What your numbers are telling you
The five-number summary
Box and whisker plot
How the values are distributed
Histogram
Headline measures
Full statistics report
| Statistic | Value | What it measures |
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Descriptive statistics, explained
Complete Descriptive Statistics
Descriptive statistics summarize a dataset using measures of central tendency (mean, median, mode) and measures of spread (standard deviation, variance, range, IQR). This calculator provides all key statistics in one place, giving you a comprehensive view of your data distribution.
Understanding Quartiles and IQR
Quartiles divide sorted data into four equal parts. Q1 (25th percentile) is the median of the lower half, Q2 is the overall median, and Q3 (75th percentile) is the median of the upper half. The Interquartile Range (IQR = Q3 - Q1) measures the spread of the middle 50% of data and is resistant to outliers.
Measures of Spread Compared
Range is the simplest (max - min) but most sensitive to outliers. Variance averages squared deviations from the mean. Standard deviation is the square root of variance (same units as data). IQR focuses on the middle 50%. Each measure serves different analytical needs depending on data characteristics.
Using Statistics in Data Analysis
Start with central tendency to understand the typical value. Check spread to understand variability. Compare mean and median, if they differ significantly, your data is likely skewed. Use quartiles to identify where data concentrates. These statistics form the foundation of any data analysis workflow.
Common questions
What is the difference between population and sample statistics?
Population statistics describe the entire group. Sample statistics estimate population parameters from a subset. The key difference is in variance/SD calculation: population divides by N, sample divides by N-1 (Bessel's correction) to avoid underestimating variability.
How do I identify outliers?
A common method uses the IQR. Values below Q1 - 1.5×IQR or above Q3 + 1.5×IQR are considered outliers. Alternatively, values with z-scores beyond ±3 are often treated as outliers.
What does it mean if the mean is much higher than the median?
This indicates a right-skewed (positively skewed) distribution, where high values pull the mean up. Income data is a classic example, a few very high incomes raise the mean above what most people earn.
How many values do I need for reliable statistics?
At minimum, you need 2 values to calculate any spread measure. For reliable results, 10+ data points are recommended. For calculating percentiles and quartiles meaningfully, at least 8-10 values are ideal.
What is the coefficient of variation?
The coefficient of variation (CV) is the standard deviation divided by the mean, expressed as a percentage. It allows comparing variability between datasets with different units or scales. A CV under 15% generally indicates low variability.
For learning and planning only. Quartile and percentile values can vary slightly between software packages because there is more than one accepted method. This tool uses the median-of-halves (Tukey) method for quartiles and the sample or population formula you select for variance.