Box Plot Chart

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Data

Data input mode

Values (one per line)

Display Settings

Orientation

Whisker type

Box width: 50%

Box fill opacity: 70%

Value axis label

Category axis label

Show mean marker

Show outliers

Show grid

Show axis labels

Show legend

Free Online Box Plot Chart Maker

What is a Box Plot Chart?

A box plot (also called a box-and-whisker plot) summarizes a dataset using five statistics: minimum, first quartile (Q1), median, third quartile (Q3), and maximum. It reveals how data is distributed — its spread, skew, and central tendency — in a single compact visual. Use it when you need to compare distributions across groups or surface outliers that a simple average would hide.

Key Features

Auto-calculate from raw values

Paste in raw numbers and the chart computes quartiles, median, and whiskers automatically — no manual statistics needed.

Tukey or min/max whiskers

Choose Tukey whiskers (1.5 × IQR) for standard outlier detection, or min/max whiskers to show the full data range.

Outlier visualization

Outlier points are plotted individually beyond the whiskers, so extreme values are visible without distorting the box.

Mean marker overlay

Optionally display the mean alongside the median to highlight skewness and asymmetry in your distribution.

Multi-group comparison

Add multiple categories side by side to compare distributions across departments, cohorts, or time periods at a glance.

Horizontal or vertical layout

Switch orientation to fit your slide or report — vertical for time-series groups, horizontal for long category labels.

Best For

Comparing test score distributions across classrooms or schools

Analyzing salary ranges by department or job level

Quality control and manufacturing process monitoring

Clinical trial result comparisons across treatment groups

Sports performance data across teams or seasons

Survey response distributions for Likert-scale questions

When to Use

When you need to show data spread, not just an average or total
When comparing distributions across two or more groups side by side
When outliers are meaningful and shouldn't be hidden in an aggregate
When your dataset has at least 5-10 values per group to make quartiles meaningful
When you need to reveal skewness or asymmetry in your data
When side-by-side histograms would be too cluttered for your audience

Common Mistakes

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Using a box plot with fewer than 5 data points per group — quartiles become misleading at small sizes
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Forgetting to label axes with units, leaving readers unsure what the values represent
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Choosing min/max whiskers when a few extreme values stretch them to the point of hiding the box
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Comparing groups with very different sample sizes without disclosing the difference to readers
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Using a bar chart when you want to show distribution, not just the mean
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Treating outliers as errors to remove rather than investigating what caused them