Box Plot Generator
Paste your data, compare up to five groups side by side, and get a publication-ready box and whisker chart with a full statistical summary. Free, instant, no sign-up required.
Accepts integers and decimals. Separate values with commas, spaces, or new lines. At least 4 values recommended.
Chart options
Box and whisker chart
Statistical summary
Quartiles use linear interpolation (Type 7 method), the same default used by Excel, R, Python NumPy, and SPSS. Outliers are values more than 1.5 times the interquartile range beyond Q1 or Q3 (Tukey fences). Whiskers extend to the most extreme non-outlier value. Standard deviation shown is the sample standard deviation (denominator n - 1).
Enter your data and read your distribution in seconds.
A box plot summarizes your data in five key numbers and makes it easy to compare groups at a glance.
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Paste or type your numbers into the data field. Values can be separated by commas, spaces, or new lines. The tool accepts both integers and decimals. To compare multiple groups, click Add Group and fill in each group's values separately. You can compare up to five groups at once.
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Give each group a descriptive name, such as "Control" and "Treatment," or "Class A" and "Class B." This label appears on the chart axis and in the stats table. If you leave the name blank, the group is labeled automatically.
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Choose your chart options. Enable Show outliers to mark extreme values as open circles beyond the whiskers. Enable Show mean marker to add a diamond at the arithmetic mean. Enable Show data points to overlay each raw value as a faint dot in the background.
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Click Generate Box Plot. The chart and the full statistical summary appear instantly. Use Download SVG to save a scalable vector image for a report or presentation, or use Copy stats to grab the numbers in a tab-separated format you can paste directly into a spreadsheet.
How to read a box plot
Each box plot is built from five values derived from your data, known as the five-number summary.
Median line
The middle value
The horizontal line inside the box. Exactly half of your values fall below this point and half above it. Unlike the mean, the median is not affected by extreme values.
The box (IQR)
The middle 50%
The box spans from Q1 (25th percentile) to Q3 (75th percentile). This range is the interquartile range (IQR) and contains the central half of your data. A tall box means more spread; a short box means values cluster tightly.
Whiskers
The spread excluding outliers
Lines extending from the box to the most extreme values that are still within 1.5 times the IQR of the box edges. Whiskers show the typical range of your non-extreme values.
Outlier circles
Extreme individual values
Open circles plotted beyond the whisker ends. A value is flagged as an outlier when it falls more than 1.5 times the IQR below Q1 or above Q3. This is the Tukey rule, used by default in R, Python, and most statistics software.
Mean marker
Optional average indicator
When enabled, a small diamond marks the arithmetic mean. If the mean sits above the median, the distribution skews right. If below the median, it skews left. A large gap between the two signals the presence of strong outliers.
Comparing groups
Side-by-side analysis
When groups overlap very little, their distributions are clearly different. Overlapping boxes suggest similar central values. Look at the median position and box size together to judge both location and spread across groups.
Tips for getting the most out of this tool
A few quick adjustments can make your chart significantly more informative.
Use meaningful group names
- Names like "Before Treatment" and "After Treatment" communicate the story in the chart itself.
- Include sample size in the name (e.g., "Class A, n=28") when groups differ greatly in size.
- Shorter names prevent label overlap on smaller screens.
Interpreting small samples
- With fewer than 10 data points, quartile estimates are unstable. Enable "Show data points" to see the raw values alongside the box.
- A single extreme value can dramatically widen the whiskers in a small sample. The outlier count in the stats table will flag it.
Frequently Asked Questions
What is a box plot used for?
A box plot (also called a box and whisker chart) displays the distribution of a numerical dataset and makes it easy to compare multiple distributions side by side. It is especially useful for spotting skewness, the spread of data, and potential outliers at a glance without reading individual values. Researchers use them to compare experimental groups, teachers use them to compare class performance, and analysts use them to compare metrics across business segments or time periods.
How many data points do I need?
A box plot can technically be drawn from as few as 4 or 5 values, but the result becomes more meaningful as your sample size grows. With fewer than 10 points, the quartile estimates can be unstable and individual outliers have an outsized effect on the shape. With 20 or more data points the chart gives a dependable picture of the distribution. This tool accepts any number of values per group; very small groups are noted in the stats table so you can interpret them with appropriate caution.
What method is used to calculate quartiles?
This tool uses linear interpolation, also called the Type 7 or inclusive method. It is the default used by Microsoft Excel (QUARTILE.INC), R, Python NumPy, SPSS, and most modern statistics software. The formula positions the quartile at the (p * (n - 1)) index in the sorted data and linearly interpolates between the two surrounding values. This produces consistent, smoothly varying quartile estimates across different sample sizes and matches the results you would get from those applications.
What counts as an outlier?
This generator uses the Tukey fence method: a value is flagged as an outlier if it falls below Q1 - 1.5 * IQR or above Q3 + 1.5 * IQR, where IQR is Q3 minus Q1. These boundaries are called the inner fences. Values between the inner and outer fences (Q1 - 3 * IQR to Q3 + 3 * IQR) are mild outliers; values beyond the outer fence are extreme outliers. The tool marks all values beyond the inner fence as open circles and draws the whisker only to the most extreme non-outlier value. Outliers are listed individually in the statistical summary table.
What is the difference between a box plot and a histogram?
A histogram divides your data into bins and shows how many values fall in each bin, making it ideal for exploring the full shape of a single distribution. A box plot compresses that information into five summary statistics, which makes it less detailed for a single dataset but far clearer for comparing multiple groups side by side. Use a histogram when you want to understand the shape of one distribution in depth; use a box plot when the goal is comparing distributions across groups or conditions.
Can I use the downloaded chart in a paper or presentation?
Yes. The downloaded file is an SVG, a scalable vector format that stays crisp at any size whether printed in a journal or scaled up on a conference slide. Most word processors and presentation tools such as Microsoft Word, PowerPoint, and Google Slides import SVG files directly. If your application requires a raster format, open the SVG in a free tool like Inkscape and export it as a high-resolution PNG or PDF.