Description: Outlier scoring is a numerical value that indicates how much an observation deviates from the expected behavior in a dataset. This concept is fundamental in anomaly detection, as it allows for the identification of data that does not conform to the norm or exhibits unusual characteristics. The score is typically calculated using statistical methods that analyze the distribution of the data, such as the mean and standard deviation. An outlier can indicate errors in data collection, natural variations in the phenomenon being studied, or even extraordinary events that warrant attention. Identifying these values is crucial across various disciplines, as they can influence the interpretation of results and decision-making. For example, in data analysis, an outlier might signal an anomaly that requires further investigation, while in quality control, it could indicate a defect in the manufacturing process. In summary, outlier scoring is a powerful tool for data analysis, enabling researchers and analysts to detect and better understand irregularities in their datasets.
