There is a lengthy process involved in data analytics, even before the actual analysis phase starts, whether one is doing it as a data analyst or in another function using data. Cleaning so-called “dirty” data, which requires editing, labor, or other manipulation before it can be analyzed, may consume up to two-thirds of the time required for data analytics. 

A data analyst may discover outliers in the “dirty” data during the cleaning stage, in which case they must be either completely removed from the dataset or handled differently. This raises the query, “What Is An Outlier Definition?”

Outlier Definition: What Does Outlier Mean?

In statistics, mathematics, and information technology, an outlier is a particular data point that is not within the range of probability for a given data collection. Stated otherwise, the outlier bears a specific distinction from the surrounding data points. In many types of analytics and research, including some that are connected to technology and IT systems, Outlier Definition analysis is quite helpful.

Outlier Definitions happen a lot in many different ways. For example, if we look at the temperatures in a factory, we might get hundreds of different temperatures ranging from 65 to 70 degrees. But we might also find one much higher temperature, like 140 degrees. In lots of situations, it’s pretty easy to spot these unusual or unexpected events with a basic analysis.

Importance Of Outliers

In statistics, an Outlier Definition can drastically alter the results of your data, particularly when attempting to determine the mean or average of a set of data when each data point has a distinct range of values. If it turns out that a mistake caused the outlier, you may have to remove it from your results in the end. Still, to understand its significance, analysis is required first. Outlier definitions may reveal discrepancies in research and data collection methods, which can assist you in improving your processes.

Outlier Definition can be helpful in a lot of different projects and assessments. Finding one number that is much bigger or smaller than the others in a test may mean there is a problem with the system. Unusual things in network security could be dangerous for a network. Usually, outliers are rare events that we can look into to figure out where they come from.

Identifying Outliers 

With a few pieces of information, it’s easy to see if there’s a mistake (for example, if the numbers are 28, 26, 21, 24, or 78, you can tell that 78 doesn’t fit in) but for lots of information or big amounts of data, we need different ways to find mistakes. We will talk about ways to find outliers using graphs or numbers. The way you use will depend on the type of data and tools you have.

Identify Outliers Using Visualizations

In data analytics, people make graphs and charts to show information in a clear way, so they can share their findings with important people. These pictures can help you see how things are changing, where they are happening, and if there are any unusual things in a lot of information shown in maps, graphs, and charts.

Identify Outliers Using Statistical Methods

A DBSCAN cluster analysis is shown in the above figure. Core points are those that surround A. Although they are density-connected through the cluster of A, points B and C are not core points; instead, they are a part of this cluster. Since Point N cannot be reached from a core point and is not a core point, it is considered noise.

By comparing a data point to the mean and standard deviation of the whole set of data points, the computation of a z-score aids in the description of any given data point. Negative standard scores show up below the mean, whereas positive standard scores show up as raw scores above the mean. A normal distribution is produced when the standard deviation is one and the mean is zero.

Types Of Outliers

There are two types of outliers:

• A univariate outlier is a very high or very low number that only affects one variable. For instance, Sultan Kösen is the tallest man alive right now. He is 8 feet, 2. 8 inches tall (251cm). This is an unusual case because it is very extreme in just one factor: height.
• A multivariate outlier is when there are unusual or extreme values for at least two things at the same time. For instance, if you are looking at how tall and heavy a group of grown-ups are, you might notice that one person in your data is 5 feet 9 inches tall, which is a normal measurement for this variable. You can also see that this person is 110 pounds. Once again, this observation is within the expected range for the weight.

When Should You Remove Outliers?

As part of the data-cleaning process, it can seem reasonable to wish to exclude Outlier Definition. However, in practice, it’s occasionally preferable, even imperative, to retain outliers in your dataset. Eliminating outliers based only on where they fell on the extremes of your dataset might lead to inconsistent findings, which would be detrimental to your aspirations as a data analyst. The statistical significance of an analysis may be diminished as a result of these discrepancies. 

The p-value tells you if your results are likely just a random occurrence and the p-value of less than 0.05 means that there is very little chance that the results happened by luck. This is strong evidence against the null hypothesis. Your results are statistically significant in this instance. Your results are not very important from a statistical point of view if the p-value is higher than 0.05. They may have happened by accident. 

Final Note!

We have discussed the fundamental Outlier Definition and potential classifications for it in this article. After that, we went over a few popular techniques for spotting outliers, how they may appear in a dataset, and whether or not it makes sense to eliminate them to get insightful information for your company. The interesting and occasionally challenging process of handling outliers adds to the excitement of the data analytics industry!