An **outlier** is an observation that lies abnormally far away from other values in a dataset. Outliers can be problematic because they can affect the results of an analysis.

One common way to find outliers in a dataset is to use the **interquartile range**.

The interquartile range, often abbreviated IQR, is the difference between the 25th percentile (Q1) and the 75th percentile (Q3) in a dataset. It measures the spread of the middle 50% of values.

One popular method is to declare an observation to be an outlier if it has a value 1.5 times greater than the IQR or 1.5 times less than the IQR.

This tutorial provides a step-by-step example of how to find outliers in a dataset using this method.

**Step 1: Create the Data**

Suppose we have the following dataset:

**Step 2: Identify the First and Third Quartile**

The first quartile turns out to be **5** and the third quartile turns out to be **20.75**.

Thus, the interquartile range turns out to be 20.75 -5 = **15.75**.

**Step 3: Find the Lower and Upper Limits**

The lower limit is calculated as:

Lower limit = Q1 – 1.5*IQR = 5 – 1.5*15.75 = **-18.625**

And the upper limited is calculated as:

Upper limit = Q3 + 1.5*IQR = 20.75 + 1.5*15.75 = **44.375**

**Step 4: Identify the Outliers**

The only observation in the dataset with a value less than the lower limit or greater than the upper limit is **46**. Thus, this is the only outlier in this dataset.

**How to Find Outliers in Practice**

The following tutorials explain how to find outliers using the interquartile range in different statistical software:

How to Find Outliers in Excel

How to Find Outliers in R

How to Find Outliers in Python

How to Find Outliers in SPSS