What is Outlier?
An outlier is also a data point that is drastically different from the other records in the dataset, with the differences being either too high or too low when compared to the rest of the observations. These extreme values are one of the reasons why giving out correct results based on the prepared analysis may be out of order if the statistical values aren’t precisely identified and addressed. Outliers may occur as a result of different reasons, e.g., measurement error, experimental variability, or genuine anomalies in the data.
Outlier finding plays an important role on all levels and in any case, where the accuracy and objectivity of statistical conclusions are of great importance. Much like the interquartile range (IQR), Z-score formulas are generally employed to locate outliers, thus giving the analysts an insight into the data’s distinctive features and allowing them to come up with enlightened decisions based on the trusted data.
Definition of Outlier
An outlier is a data point that lies outside the overall pattern of a dataset, significantly differing from other observations.
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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