How to Identify Box Plot Outliers? Easy Steps

A Box Plot is the visualization design we recommend if your goal is to display quartiles, mean, and outlier attributes in data.

So, what is an outlier?

An outlier is a value that lies at the extremes of a data series and thus can affect the overall observation. Outliers are also termed as extremes because they lie on either end of the data.

A Box Plot Outliers detector is easy to interpret, even for non-technical audiences.

Excel lacks Box Plot Charts. And this means you’ve got to use other pricey tools or plot the chart manually. So, if your goal is to display high-level insights, you’ve got to think beyond Excel.

We’re not recommending you do away with Excel, especially if your goal is to access ready-made and visually appealing chat.

Download and install a particular add-in (which we’ll mention later) into your Excel to access the ready-to-go Outliers Box Plot detector.

In this blog, you’ll learn:

Table of Content:

1. Introduction
2. What are Box Plot Outliers?
3. Video Tutorial:
4. Types of Outliers in Box Plots
5. What is the 1.5 IQR Rule?
6. How are Outliers Determined in Box Plots?
   • Formula for Outliers in Boxplot
7. Steps to Generate a Chart with Outliers
8. How to Show Outliers?
9. Advantages of Box Plot Outliers
10. FAQs
11. Wrap Up

Before addressing the how-to guide, let’s address the following question: What is an outlier?

Introduction

Introduction: An outlier is a visualization design that uses box shapes to display insights into data.

The chart simplifies bulky and complex data sets into quartiles and averages. Also, you can use the chart to pinpoint outliers in your data. The Box Plot segments key variables in quarters or (quartiles).

For instance, you can draw boxes to connect the first quartile to the third quartile. In this case, the boxes will represent the average values of key data points.

Lines are used to identify numbers that fall outside of the average data points.

The chart displays your data’s shape, variability, and center (or median) information. Also, you can leverage the chart to determine the skewness of data points.

Essentially, an outlier shows the following points of data:

• Minimum range
• Lower quartile
• Median
• Upper quartile
• Maximum range

Besides the five summary numbers, the visualization displays the following:

• Minimum score
• Median
• Maximum Score
• Lower Quartile
• Upper Quartile

Let’s check out the elements in detail.

The minimum score is the lowest score, excluding outliers (shown at the end).

Lower Quartile: 25% of all variables fall below the lower quartile value.

The median is the mid-point of the data and is shown by the line that divides the box into two parts (sometimes known as the second quartile). Half the scores are greater than or equal to this value, and half are less.

The upper quartile is 75% of all variables that fall below the upper quartile value (also known as the third quartile). Thus, 25% of data points are above the value.

The maximum score is the highest score, excluding outliers (shown at the far right of the plot).

The upper and lower lines represent scores outside the middle 50% that is, the lowest 25% and the highest 25% of scores.

The interquartile range (IQR) ranges between the 25th and 75th percentiles.

An Outlier Graph can help you visualize large datasets. More so, you can easily detect the symmetry of the data at a glance by using the chart. Unlike other data visualization techniques, to display outliers.

The visualization design is best suited for comparing distributions between key groups in data. The charts are compact in design to help you display a ton of information without clutter.

Data sets can sometimes contain outliers that are suspected to be anomalies (perhaps because of data collection errors or just plain old flukes).

If outliers are present, the line on the appropriate side is drawn to 1.5 × IQR rather than to the data minimum or maximum. Small circles or unfilled dots are drawn on the chart, such as in a dot plot, to indicate where suspected outliers lie. On the other hand, filled circles are used for known outliers.

What are Box Plot Outliers?

Definition: An outlier is a value that lies at both extremes of the data. In other words, it’s a value that lies outside the overall distribution pattern and thus can affect the overall data series.

These anomalies are treated as abnormal values that can distort the final insights.

Data visualization experts agree that a value should be regarded as an outlier if it’s 1.5 times bigger or smaller than the expected observation.

Video Tutorial:

In this video, you’ll learn how to create a Boxplot Column Chart in Excel.

In the coming section, we’ll address the following question: What is the 1.5 IQR rule?

Types of Outliers in Box Plots

• Mild Outliers: These are data points that lie between 1.5 and 3 times the interquartile range (IQR) above the third quartile or below the first quartile. They are often represented as open circles or small dots.
• Extreme Outliers: These are data points that lie more than 3 times the IQR above the third quartile or below the first quartile. They are typically depicted as asterisks or different symbols to distinguish them from mild outliers.

What is the 1.5 IQR Rule?

To understand the 1.5 IQR rule, we’ll cover the interquartile range, abbreviated as the IQR.

The interquartile range is just the width of the box in the chart.

In other words, IQR = Q3 – Q1.

The IQR measures how key data points are spread out. Therefore, an outlier is 1.5 multiplied by the IQR value of your data.

Keep reading to discover how to use the Box Plot outlier Diagram to identify outliers. You don’t want to miss this.

How are Outliers Determined in Box Plots?

An outlier plot shows the distribution of key data points along a number line.

You can generate the chart by ordering a data set to find the median, upper and lower quartiles, and upper and lower extremes.

To calculate values, such as the mean, follow the steps below:

• Order the data from least to greatest.
• Find the median or middle value that splits the data set into two equal groups. If there is no middle value, use the average of the two middle values as the median.
• Find the median for the lower half of the data set.
• Find the median for the upper half of the data set.
• Use these five values to create a visual representation displaying the following: lower extreme, lower quartile, median, upper quartile, and upper extreme.
• Draw vertical lines through the lower quartile, median, and upper quartile.
• Form a box by connecting the vertical lines from the lower quartile, median, and upper quartile.
• Draw lines extending from the edges of the box to represent the range of the data.

Formula for Outliers in Boxplot

The formulas for identifying outliers are based on the interquartile range (IQR). The IQR is the range between the first quartile (Q1) and the third quartile (Q3).

1. Lower Bound for Mild Outliers:
   $Q1-1.5—IQR$
2. Upper Bound for Mild Outliers:
   $Q3+1.5—IQR$
3. Lower Bound for Extreme Outliers:
   $Q1-3—IQR$
4. Upper Bound for Extreme Outliers:
   $Q3+3—IQR$

Steps to Generate a Chart with Outliers

Excel is one of the go-to data visualization tools for businesses and professionals.

However, this freemium spreadsheet tool does not natively support the Outliers Diagram. In other words, you’ll never find this visualization design in Excel.

Well, you don’t have to do away with the spreadsheet app.

You can turn Excel into a reliable data visualization tool loaded with ready-made and visually stunning outlier plot examples by installing third-party apps, such as ChartExpo.

How to read an Outlier in Excel should never be a stressful task. Keep reading to learn more.

Why ChartExpo?

ChartExpo is an add-in you can easily install in your Excel.

With a large number of advanced visualizations, ChartExpo turns your complex, raw data into compelling, easy-to-decode visual renderings that tell the story of your data.

The application produces simple, ready-to-go, and clear visualization designs with just a few clicks.

Yes, ChartExpo generates ready-made visual summaries that are amazingly easy to interpret, even for non-technical audiences.

In the coming section, you’ll see how to create one using ChartExpo to identify outliers. You don’t want to miss this.

Example

This section will use the outlier plot in Excel to find the outlier in the data below.

Gender	Age
Male	29
Male	34
Male	37
Male	28
Male	45
Male	55
Male	36
Male	28
Male	43
Male	35
Male	45
Male	34
Male	31
Male	32
Male	34
Male	30
Male	36
Male	42
Male	32
Male	48
Male	48
Male	27
Male	31
Male	24
Male	78
Female	22
Female	38
Female	40
Female	25
Female	23
Female	36
Female	37
Female	49
Female	44
Female	26
Female	41
Female	30
Female	38
Female	45
Female	40
Female	25
Female	38
Female	26
Female	44
Female	39
Female	43
Female	44
Female	48
Female	31
Female	33
Female	38
Female	37
Female	38
Female	43
Female	67
Female	76
Female	80

Click here to install ChartExpo into your Excel. Once you’re done, follow the easy steps below to create an outlier chart in Excel.

• Open your Excel and paste the table above.
• Open the worksheet and click the Insert Menu button.
• Click the My Apps button and then click the See All, as shown below.

• Click the Insert button to get started with ChartExpo.

• Look for the “Your Required Chart” in the list of charts.
• Once the Chart pops up, click on its icon to get started, as shown below.

• Select the sheet holding your data and click the Create Chart From Selection.

• Check out the final Box Plot Outlier Chart below.

How to Show Outliers?

Insights

• The ages between 68 and 85 years were outliers.
• The maximum and minimum ages among the males were 26 and 57 years, respectively.
• The maximum and minimum ages among the females were 20 and 48 years, respectively.
• The median age of male respondents is 39 years. Conversely, the median age of females is 42 years.

Advantages of Box Plot Outliers

Check out the benefits of the chart below:

• An outlier-focused chart can help you visualize large datasets.
• You can easily detect the symmetry of the data at a glance by using the chart.
• Unlike other data visualization techniques, this method displays outliers..

FAQs

Why is 1.5 IQR equal to Box Plot outlier?

John Tukey was the first person to use Box Plot outliers to display insights into data. He came up with the 1.5 IQR requirement to pinpoint outliers.

The IQR measures how key data points are spread out. Therefore, an outlier is 1.5 multiplied by the IQR value of your data.

What causes an outlier?

An outlier is a value that lies at both extremes of the data.

In other words, it’s a value that lies outside the overall distribution pattern and thus can affect the overall data series.

Outliers are caused by the following:

• System behavior
• Fraudulent behavior
• Human and instrument errors

Wrap Up

As we said, a Box Plot Outlier visualization is ideal if your goal is to display quartiles, mean, and highlight extreme values in your dataset.

So, what is an outlier?

An outlier is a value that lies at the extremes of a data series and can significantly impact overall analysis. These values are often called extremes because they fall far from the rest of the data.

A Box Plot Outlier detector is easy to interpret, even for non-technical audiences.

Unfortunately, Excel lacks built-in support for this type of chart.

The only viable options are using other pricey data visualization tools or plotting the chart manually.

If you aim to clearly show data attributes like mean, outliers, and quartiles, it’s time to think beyond spreadsheets.

So, what’s the solution?

We recommend installing third-party tools like ChartExpo in Excel to access ready-made charts that highlight Box Plot Outliers effectively.

ChartExpo is an add-in that’s simple to download and install. It provides intuitive, easy-to-read visuals—no coding required.

Understanding how to identify outliers visually shouldn’t be complicated.

Sign up for a 7-day free trial today and explore ready-made charts designed for clarity and impact.