Box and Whisker Plot Maker

This calculator and generator for box and whisker plot will visualize your dataset’s five-number summary, outliers, and distribution spread.

Enter Your Dataset:

Tip: Paste from spreadsheets, use commas, spaces, or newlines. Negative and decimal numbers work too.

Outlier Method:

1.5 × IQR (Standard) 3 × IQR (Extreme only)

Box and Whisker Plot

Whiskers

IQR Box

Median

Outliers

Minimum

—

Q1 (25th %ile)

—

Median (Q2)

—

Q3 (75th %ile)

—

Maximum

—

Interquartile Range (IQR) Analysis

0%25%50%75%100%

IQR Value

—

Q3 − Q1

Total Range

—

Max − Min

IQR / Range

—

Middle 50% spread

Whisker Boundaries & Fences

Outer Lower

—

Q1 − 3×IQR

Inner Lower

—

Q1 − 1.5×IQR

Lower Whisker

—

Actual data point

Upper Whisker

—

Actual data point

Inner Upper

—

Q3 + 1.5×IQR

Outer Upper

—

Q3 + 3×IQR

Outlier Detection

Outliers Found

Mild: 0

Extreme: 0

Lower Outliers

None

Upper Outliers

None

Distribution Shape Analysis

Left SkewedSymmetricRight Skewed

—

Skewness

—

Bowley coefficient

Lower Whisker Len

—

Med − Lower

Upper Whisker Len

—

Upper − Med

Central Tendency Measures

Median

—

Mean

—

Trimean

—

Midhinge

—

Spread & Variability

Std Deviation

—

Variance

—

MAD

—

Key Percentiles

P5—

P10—

P25—

P50—

P75—

P90—

P95—

Dataset Overview

Count

—

Sum

—

Unique

—

Midrange

—

Sorted Data

Construction Steps

Find median (Q2)

Find Q1 and Q3

Calculate IQR

Determine fences

Find whiskers & outliers

Interpreting Your Box Plot

📦 The Box

Contains middle 50% of data
Width = IQR (spread measure)

📏 Whiskers

Extend to non-outlier extremes
Max = 1.5×IQR from box

➖ Median Line

Position shows skewness
Centered = symmetric data

⚬ Outliers

Points beyond whiskers
May need investigation

Export Options

Box plots are excellent for comparing distributions and identifying outliers at a glance.

Defining a Box and Whisker Plot

A box and whisker plot (also called a box plot) is a standardized way to display data distribution based on five key values: minimum, first quartile (Q1), median, third quartile (Q3), and maximum. Developed by statistician John Tukey in 1969, it provides a visual summary that reveals the center, spread, and skewness of your data at a glance.

How to Read a Box Plot

The rectangular “box” contains the middle 50% of your data, while the “whiskers” extend to show the range of typical values. Any points beyond the whiskers are potential outliers worth investigating.

Component	What It Shows
Box width	Interquartile range (middle 50%)
Median line position	Data symmetry or skewness
Whisker lengths	Spread of typical values
Individual points	Outliers requiring attention

How it’s Compiled

Every box plot is built from these five statistics:

Minimum – smallest non-outlier value
Q1 – 25th percentile (lower quartile)
Median – 50th percentile (middle value)
Q3 – 75th percentile (upper quartile)
Maximum – largest non-outlier value

Calculating the Interquartile Range

The IQR measures the spread of the middle half of your data:

IQR = Q3 − Q1

Identifying Outliers

Values are flagged as outliers when they fall outside the “fences”:

Mild outliers: Beyond Q1 − 1.5×IQR or Q3 + 1.5×IQR
Extreme outliers: Beyond Q1 − 3×IQR or Q3 + 3×IQR

When to Use Box/Whisker Plots

Box plots excel at comparing multiple datasets side-by-side and quickly identifying outliers. They’re commonly used in scientific research, quality control, test score analysis, and financial reporting where understanding data spread matters as much as the average.