**What is outlier in simple words?** : Outliers explained . An observation that differs abnormally from other values in a population-based random sample is referred to as an outlier. In a sense , the choice of what constitutes abnormality is left up to the analyst (or a consensus process) in accordance with this definition.

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Outliers can provide insightful information about the data you’re analyzing and can have an impact on statistical findings. You might be able to find inconsistencies and spot any mistakes in your statistical processes by doing this.

Let’s get started!

A data point that is an outlier in a data graph or dataset you are working with is one that is extraordinarily high or extraordinarily low in comparison to the nearest data point and the rest of the neighboring coexisting values.

Extreme values that significantly deviate from the dataset’s or graph’s normal distribution are known as outliers.

There is an outlier in the graph below, on the extreme left.

Comparing January’s value to the other months, it is significantly lower.

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Table of Contents

## How to Identify an Outlier in a Dataset

Alright, how do you go about finding outliers?

An outlier has to satisfy either of the following two conditions:

`outlier < Q1 - 1.5(IQR)`

`outlier > Q3 + 1.5(IQR)`

The rule for a low outlier is that a data point in a dataset has to be less than`Q1 - 1.5xIQR`

.

This implies that a data point must deviate by more than 1. To be regarded as a low outlier, the interquartile range must be five times that of the first quartile.

The rule for a high outlier is that if any data point in a dataset is more than `Q3 - 1.5xIQR`

, it’s a high outlier.

The data point needs to fall more than 1 specifically. To be deemed a high outlier, the interquartile range must be five times the third quartile’s median.

As you can see, there arecertain individual values you need to calculate first in a dataset, such as the `IQR`

. But to find the `IQR`

, you need to find the so called first and third quartiles which are `Q1`

and `Q3`

respectively.

So, let’s see what each of those does and break down how to find their values in both an odd and an even dataset.

## How to Find the Upper and Lower Quartiles in an Odd Dataset

To get started, let’s say thatyou have this dataset:

`25,14,6,5,5,30,11,11,13,4,2`

The first step is to **sort the values in ascending numerical order**,from smallest to largest number.

`2,4,5,5,6,11,11,13,14,25,30`

The lowest value (**MIN**) is `2`

and the highest (**MAX**) is `30`

.

### How to calculate `Q2`

in an odd dataset

The next step is to find the **median** or quartile 2 (Q2).

This particular set of data has an odd number ofvalues, with a total of `11`

scores all together.

When you search for the median in a dataset, you are looking for the single middle number in the set’s middle value.

In odd datasets, there in only one middle number.

Since there are `11`

values in total, an easy way to do this is to split the set in two equal parts with each side containing `5`

values.

The median value will have `5`

values on one side and `5`

values on the other.

`(2,4,5,5,6)`

,** 11** ,

`(11,13,14,25,30)`

The median is `11`

as it is the number that separates the first half from the second half.

The following is an alternative method to confirm your accuracy:

`(total_number_of_scores + 1) / 2`

.

This is `(11 + 1) /2 = 6`

, which means you want the number in the `6th`

place of this set of data – which is `11`

.

So `Q2 = 11`

.

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### How to calculate `Q1`

in an odd dataset

Next, to find the lower quartile, `Q1`

, we need to find the median of the first half of the dataset, which is on the left hand side.

As a reminder, the initial dataset is:

`(2,4,5,5,6)`

, ** 11** ,

`(11,13,14,25,30)`

The first half of thedataset, or the lower half, does not include the median:

`2,4,5,5,6`

This time, there is again an odd set of scores – specifically there are `5`

values.

You want to again split this half set into another half, with an equal number of two values on each side. You’ll get a unique number, which will be the number in the middle of the `5`

values.

Pick the middle value that stands out:

`(2,4)`

,** 5**,

`(5,6)`

In this case it’s`Q1 = 5`

.

To double check, you can also do `total_number_of_values + 1 / 2`

, similar to the previous example:

`(5 + 1) /2 = 3`

.

This means you want the number in the 3rd place, which is `5`

.

### How to calculate `Q3`

in an odd dataset

To find the upper quartile, Q3, the process is the same as for `Q1`

above. But in this case you take the second half on the right hand side of the dataset, above the median and without the median itself included:

`(2,4,5,5,6)`

,** 11** ,

`(11,13,14,25,30)`

`11,13,14,25,30`

You split this half of the odd set of numbers into another half to find the median and subsequently the value of `Q3`

.

Like you did in the first half, you want the number to once more be in third place.

`(11,13)`

,** 14**,

`(25,30)`

So `Q3 = 14`

.

### How to calculate `IQR`

in an odd dataset

The IQR, or interquartile range, will now be calculated as the next step.

The distance or difference between the lower quartile (Q1) and the upper quartile (Q3) is shown here.

As a reminder, the formula to do so is the following:

`IQR = Q3 - Q1`

To find the IQR of the dataset from above:

`IQR= 14 - 5IQR = 9`

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### How to find anoutlier in an odd dataset

To recap so far, the dataset is the one below:

`2,4,5,5,6,11,11,13,14,25,30`

and so far, you have calucalted the five number summary:

`MIN = 2Q1 = 5MED = 11Q3 = 14MAX = 30`

Let’s check the dataset for any outliers before we conclude.

As a reminder, an outlier must fit the following criteria:

`outlier < Q1 - 1.5(IQR)`

Or

`outlier > Q3 + 1.5(IQR)`

To see if there is a lowest value outlier, you need to calculate the first part and see if there is a number in the set that satisfies the condition.

`Outlier < Q1 - 1.5(IQR)Outlier < 5 - 1.5(9)Outlier < 5 - 13.5 outlier < - 8.5`

There are nolower outliers, since there isn’t a number less than `-8.5`

in the dataset.

Next, to see if there are any higher outliers:

`Outlier > Q3 + 1.5(IQR)=Outlier > 14 + 1.5(9)Outlier > 14 + 13.5Outlier > 27,5`

And there is a number in the dataset that is more than `27,5`

:

`2,4,5,5,6,11,11,13,14,25,`

`30`

In this case, `30`

is the outlier in the existing dataset.

## How to Find the Upper and Lower Quartiles in an Even Dataset

What happens if your dataset only contains an even distribution of data?

There isn’t a single stand-out median (Q2), upper quartile (Q1), or lower quartile (Q3).

So the process of calculating quartiles and then finding an outlier is a bit different.

### How to calculate `Q2`

in an even dataset

Say that you have this dataset with `8`

numbers:

`10,15,20,26,28,30,35,40`

This time, the numbers are already arranged from lowest to highest value.

Tofind the **median** number in an even dataset, you need to find the value that would be in between the two numbers that are in the middle. You add them together and divide them by `2`

, like so:

`10,15,20`

,** 26,28**,

`30,35,40`

`26 + 28 = 5454 / 2 = 27`

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### How to calculate `Q1`

in an even dataset

To calculate to upper and lower quartiles in an even dataset, you keep all the numbers in the dataset (as opposed to in the odd set you removed the median).

This time, the dataset is cut in half.

`10,15,20,26 | 28,30,35,40`

To find `Q1`

, you split the first half of the dataset into another half which leaves you with a remaining even set:

`10,15 | 20,26 `

To find the median of this half, you take thetwo numbers in the middle and divide them by two:

`Q1 = (15 + 20)/2Q1 = 35 / 2Q1 = 17,5`

### How to calculate `Q3`

in an even dataset

To find `Q3`

, you need to focus on the second half of the dataset and split that half into another half:

`28,30,35,40`

-> `28,30 | 35,40`

The two numbers in the middle are `30`

and `35`

.

You add them and divide them by two, and the result is:

`Q3 = (30 + 35)/2Q3 = 65 / 2Q3 = 32,5`

### How to calculate the`IQR`

in an even dataset

The formula for calculating IQR is exactly the same as the one we used to calculate it for the odd dataset.

`IQR = Q3 - Q1IQR = 32,5 - 17,5IQR = 15`

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### How to find an outlier in an even dataset

As a recap, so far the five number summary is thefollowing:

`MIN = 10Q1 = 17,5MED = 27Q3 = 32,5MAX = 40`

To calculate any outliers in the dataset:

`outlier < Q1 - 1.5(IQR)`

Or

`outlier > Q3 + 1.5(IQR)`

To find any lower outliers, you calcualte `Q1 - 1.5(IQR)`

and see if there are any values less than the result.

`outlier < 17,5 - 1.5(15)=outlier < 17,5 - 22,5outlier < -5`

There aren’t any values in the dataset that are less than `-5`

.

Finally, to find any higher outliers, you calculate ` Q3 - 1.5(IQR)`

and see if there are any values in the dataset that are higher than the result

`outlier > 32.5 + 1.5(15)=outlier > 32.5 + 22.5outlier > 55`

There aren’t any values higher than `55`

so this datasetdoesn’t have any outliers.

## Conclusion

This article taught you how to calculate any outliers in a dataset by determining the interquartile range.

On freeCodeCamp’s YouTube channel, there is a free 8-hour university course you can take if you want to learn more about statistics and the fundamentals of data science.

Thank you for for reading and happy learning.

Take a free coding course. More than 40,000 people have found employment as developers thanks to freeCodeCamp’s open source curriculum. starting now

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**What is a outlier person?**: a member of a group who differs from the majority of members through actions, attitudes, or religious convictions: climate change scientists who hold outlier positions.

**What is outliers and example?**: A value that “lies outside” (is much smaller or larger than) most of the other values in a set of data. For example in the scores 25,29,3,32,85,33,27,28 both 3 and 85 are “outliers”.

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This observation or piece of data deviates from the norm. An outlier in a scientific study may be very different from other data points the scientist has collected. Outliers in data sets are occasionally removed by scientists.

In the real world, outliers can also occur. The typical giraffe, for instance, is 4 feet tall. 8 m (16 ft) in height. While some may be a little taller or shorter, most giraffes will be about that height. However, recently, researchers discovered a young giraffe, only about 2. 7 meters (9 feet) high and another that was only 2 inches wide. 6-meter-long (8. 5 feet) These two tiny giraffes stand out as exceptions.

By accident, outliers can happen. An error or incorrect number may occasionally be recorded by a scientist. A flaw in the way the scientist is testing their hypothesis could also be indicated by outliers. Therefore, it’s critical to comprehend outliers before eliminating them.

Check out the full list of Scientists Say.

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### More Stories from Science News Exploreson Math

[/lightweight-accordion]**What is another word for outlier?** : A synonym for outlier is deviation, anomaly, exceptionality, irregularity, aberration, oddity, and weirdness.

## Additional Question — What is outlier in simple words?

### What is the opposite of outlier?

the opposite of something distinctive. normality. standard. regularity. normalcy.

### How do you determine an outlier?

Interquartile range (IQR) multiplied by one to identify outliers. Using the formula in 5, we can determine whether a particular value is an outlier. If we take away 1, then. Any data values less than 5 x IQR from the first quartile are regarded as outliers.

### How do you use the word outlier in a sentence?

The Scholarship Student Was Considered an Outlier by Her Wealthy Peers Outlier in a Sentence Because she was the only person in her small, southern town who openly declared that she was an atheist and that she liked reggae, Jenna was well known as an outlier.

### Are outliers and anomalies the same?

Outliers are observations that are distant from the mean or location of a distribution However, they don’t necessarily represent abnormal behavior or behavior generated by a different process On the other hand, anomalies are data patterns that are generated by different processes

### Where did the term outlier come from?

Outlier (which is pronounced simply out-ly-er, although it looks vaguely French) was originally, when it appeared in English in the early 17th century, simply another word for outsider, nonconformist, or weirdo An outlier was, in the words of the Oxford English Dictionary, an individual whose origins,

### What is an outlier in business?

A time series plot demonstrates what is taking place in your company. That may occasionally not take place the way you anticipate it should. It is an outlier when the divergence is outside the normal range of variance.

### What is a real life example of an outlier?

In a real-world example, the average height of a giraffe is about 16 feet tall However, there have been recent discoveries of two giraffes that stand at 9 feet and 8 5 feet, respectively These two giraffes would be considered outliers in comparison to the general giraffe population

### What are the three different types of outliers?

In statistics and data science, there are three generally recognized categories into which all outliers fall: Type 1: Global outliers (also known as point anomalies); Type 2: Contextual (conditional) outliers; and Type 3: Collective outliers.

### What are the characteristics of an outlier?

An outlier is defined as a point that lies very far from the mean of the corresponding random variable This distance is measured with respect to a given threshold, usually a number of times the standard deviation

### What problems can outliers cause?

The normality may be reduced if the outliers are not distributed randomly. The power of statistical tests is decreased and the error variance is increased. They may skew results and/or have an impact on estimates. They may also have an effect on the fundamental presumption in statistical models like regression.

### Why is outlier important?

Anomalies, also known as outliers, can exist for a number of reasons, so it’s critical to find and investigate the true outliers. A wealth of knowledge can be found in outliers. They provide you with a special insight into a circumstance. Understanding why an outlier occurs aids in better problem solving.