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An outlier in data science is an expected but occasionally frustrating occurrence for statisticians. Outliers fit well outside the pattern of a data sample, which causes confusion and needs to be addressed. This article will go over what outlying data points are, how they affect data and what options data scientists have for dealing with them.

Definition Of An Outlier

An outlier is simply a data point that is drastically different or distant from other data points. A set of data can have just one outlier or several. To be an outlier, a data point must not correspond with the general trend of the data set. It must be very noticeably outside the pattern. There are several types of outliers, including point outliers, contextual outliers, and collective outliers. A point outlier is when single data points fall outside the normal pattern of the distribution. A contextual outlier means the points’ deviation is within the same context. Collective outliers can form their own patterns that lead to new discoveries.

How Do Outliers Happen?

There are a number of reasons why outliers in data science can occur. They could be the result of a measurement error, data entry errors, errors in data processing, problems with a sample or simply a natural result in the data. For example, political polls often generate results that are far outside the expected range. Pollsters then usually average all results to gain a more accurate prediction of a race. In the case of a clear measurement error, outliers are usually discarded. However, as reported by Towards Data Science, it is very important for data scientists to use outlier detection techniques to discover the cause of the outliers before deciding what to do about them.

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How Are Outliers Dealt With?

Outliers can spoil data, so while they are to be expected, data scientists are wary of them and must respond to them correctly. There are a number of ways data scientists can handle outliers. The most common decision is whether to include or remove outlying data points. The first thing data scientists do when encountering outliers is to ensure the data was entered into the model correctly. If an error is discovered, it needs to be corrected immediately. Whether removing them is correct or not largely depends on the context and what information is desired from the data set. In some cases, outliers can safely be removed. In others, they should simply be presented with the rest of the results. It will be clear that these outliers, while legitimate, do not fit the established pattern.

How Are Outliers Helpful?

Outliers can be very helpful for data scientists. Outliers can indicate that theories might be invalid, which can force data scientists to reevaluate their preconceived notions about the source of the data they’ve collected. Outliers may also indicate that there was an issue with the way the experiment itself was set up, or point out a data entry error. Because what constitutes an outlier is so subjective, data points that may appear to be outliers may actually be within a more normal range of the data pattern.

Data scientists should not be afraid of outliers. They are going to occur and may even prove to have unseen benefits. Outliers in data science can be frustrating but also beneficial for data scientists no matter their subject of study.


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