## What is a Box Plot?

A box plot, sometimes called a box and whisker plot, provides a snapshot of your continuous variable’s distribution. They particularly excel at comparing the distributions of groups within your dataset. A box plot displays a ton of information in a simplified format. Analysts frequently use them during exploratory data analysis because they display your dataset’s central tendency, skewness, and spread, as well as highlighting outliers.

Box plots truly shine when comparing data distributions across different groups. Their compact design offers a neat summary of data, making it a breeze to compare distributional properties of the groups through the positioning of box and whisker markings.

Use a box plot to compare distributions when you have a categorical grouping variable and a continuous outcome variable. The levels of the categorical variables form the groups in your data, and the researchers measure the continuous variable. These graphs are often precursors to hypothesis tests, such as 2-sample t-tests and ANOVA.

When you’re assessing one distribution, use a histogram because it offers a more detailed view. For more information, see Using Histograms to Understand Your Data.

**Related post**: Data Types

## Anatomy of a Box and Whisker Plot

Instead of displaying the raw data points, a box and whisker plot takes your sample data and presents ranges of values based on quartiles using boxes and lines. Additionally, they display outliers using asterisks that fall outside the whiskers. Learn more about Quartiles: Definition, Finding & Using.

Box plots display the five-number summary. This summary includes five key data points:

- The smallest number (minimum)
- The first quartile (25% mark)
- The middle number (median)
- The third quartile (75% mark)
- The largest number (maximum)

Together, these five values highlight your data’s distribution’s shape, spread, and central tendency. All these measures are nonparametric and do not make assumptions about the data distribution. This aspect makes a box and whisker plots especially suitable for the early stages of analysis.

This graph works by breaking your data down into quartiles. When your sample size is too small, the quartile estimates might not be meaningful. Consequently, these plots work best when you have at least 20 data points per group.

Let’s look at the anatomy of a box plot before getting to an example. Notice how it divides your data into quarters—at least approximately because the upper and lower whiskers do not include outliers, which the chart displays separately.

The image below shows how a box and whisker plot compares to the probability distribution function for a normal distribution. The box itself is the interquartile range, which contains 50% of your data. Additionally, notice how each whisker contains 24.65% of the distribution rather than an exact 25%. Box plots consider the observations beyond the whiskers to be outliers.

Learn more about outliers, including how a box and whisker plot detects them, in my post 5 Ways to Find Outliers in Your Data.

## How to Read a Box Plot

A box and whisker plot allows you quickly assess a distribution’s central tendency, variability, and skewness. Let me show you how!

### Central Tendency

To compare central tendencies in a box plot, use the median line and the overall vertical placement of the boxes.

In the graph below, Group A has a higher median line than Group B. Indeed, it’s easy to see that Group A’s entire distribution is shifted upwards relative to Group B. However, Group A’s lower quartile overlaps with Group B’s upper quartile.

**Related posts**: Measures of Central Tendency and Median: Definition and Uses

### Variability

To assess variability in a box and whisker plot, remember that half your data for each group falls within the interquartile box. The longer the box and whiskers, the greater the variability of the distribution. The total length of the whiskers represents the range of the data.

In the plot below, Group 2 has more variability than Group 1 because it has a longer box and whiskers. Group 1 ranges from approximately 3 to 7 while Group 2 ranges from roughly 1.5 to 9

Learn more about Measures of Variability.

### Skewness

To determine whether a distribution is skewed in a box plot, look at where the median line falls within the box and whiskers.

You have a symmetrical distribution when the box centers approximately on the median line, and the upper and lower whiskers are about equal length. If the two sides are not roughly equivalent, your distribution is skewed.

It’s a right-skewed distribution when the median is closer to the box’s lower values and the upper whisker is longer. Notice how the long tail extends into the higher values in the box and whisker plot below, making it positively skewed.

It’s a left-skewed distribution when the median is closer to the box’s higher values, and the lower whisker is longer. Notice that the long tail extends towards the lower values, making it negatively skewed.

Learn more about Skewed Distributions.

## Box Plot Example: Comparing Groups

Let’s combine all we’ve learned about box plots and compare four groups in this example.

Suppose we have four groups of test scores and we want to compare them by teaching method. To create this graph yourself, download the CSV data file: Boxplot. Teaching method is our categorical grouping variable and Score is the continuous outcome variable that the researchers measured.

Method 1 and 2 have nearly identical medians, but Method 1 has somewhat more variability. The second method also has a high outlier that we should investigate. Method 3 has the highest variability in scores and is potentially left-skewed. Method 4 has the highest median.

WKBN Prame says

Can I get better results by transforming data to normality before making the Tukey’s Box Plot and then back calculating?

Jim Frost says

Hi,

It’s important to stay close to your original data to really understand its distribution. I recommend creating a regular box plot with untransformed data. It’s a great tool for the initial exploration of your data. See it how it is. A normal box plot also finds outliers using an approach that doesn’t depend on the normal distribution.