Anomaly Detection: How to Find Outliers Using the Grubbs Test

Anomaly Detection: How to Find Outliers Using the Grubbs Test

Anomaly Detection: How to Find Outliers Using the Grubbs Test – PyImageSearch

“In the world of data analytics, detecting anomalies is crucial for uncovering patterns that deviate from the norm. Whether it’s identifying fraudulent transactions, spotting manufacturing defects, or analyzing climate data, the ability to find outliers can significantly enhance decision-making processes. One of the robust statistical methods employed for this purpose is the Grubbs test. Named after Frank E. Grubbs, this test is specifically designed to detect a single outlier in a normally distributed dataset.

The Grubbs test works by comparing the maximum deviation of the data points from the mean relative to the standard deviation. It effectively identifies whether an extreme value within the dataset is indeed an outlier or just a part of the natural variation.

In this blog post, we will delve into the mechanics of the Grubbs test, its application in anomaly detection, and provide a practical guide on how to implement it using real-world data…”

Source: pyimagesearch.com/2025/01/06/anomaly-detection-how-to-find-outliers-using-the-grubbs-test/