Types of Outlier
Outliers can be categorized as extreme and mild based on their deviation from the dataset’s central tendency.
Extreme Outlier
Data points that lie far from the mean or median, typically beyond 3 times the interquartile range (IQR).
Formula:
Outlier = Q3 + 3 × IQR
Example:
In a dataset with Q3 = 20, Q1 = 10, and IQR = 10, an extreme outlier would be any value above 50.
Mild Outlier
Data points that are moderately different from the rest of the data, falling between 1.5 to 3 times the IQR from the quartiles.
Formula:
Outlier = Q3 + 1.5 × IQR
Example:
In the same dataset, a mild outlier would fall between 20 and 35.
Outlier
Outliers stand for data points that are indicative of a much higher variability than other observations in a given dataset. This can result in skewing statistical studies and wrong conclusions after all the variables are not adequately identified and handled. Identifications of outliers are very relevant for the financial sector, healthcare industry and decision-making processes that depend on data analysis.
In this article, we will learn in detail about outlier, its definition, examples, types, how to find outlier, their uses and how they are different of inliers.
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