Why outliers are more than just statistical noise in your data analysis

Outliers are data points that defy expected patterns. Understanding their influence is essential for accurate analysis, as these anomalies can significantly alter the perceived relationship between variables in a regression model.

An outlier is defined as an observation that deviates from the standard pattern of a dataset. Examples range from physical extremes, such as a teenager standing 6'10", to performance anomalies, like a competitor consuming 60 hot dogs in a single contest.

In the context of regression analysis, these points are critical because they can exert a disproportionate impact on the results. Recognizing how outliers shift the relationship between variables is a fundamental step in ensuring statistical models remain robust and representative.

Source: Outliers