A data point that’s way off from the rest—could be an error, or could be the next big discovery.
A statistical outlier is a data point that significantly deviates from the other observations in a dataset. These anomalies can arise due to variability in the measurement or may indicate experimental errors. Outliers are critical in data science and artificial intelligence as they can skew statistical analyses, leading to misleading conclusions. Identifying and handling outliers is essential for ensuring the integrity of data-driven insights, as they can affect the performance of machine learning models and the accuracy of predictive analytics. Data scientists, data analysts, and machine learning engineers must be adept at recognizing outliers and employing appropriate techniques for their detection and treatment, such as Z-scores, IQR (Interquartile Range), or visual methods like box plots.
Outliers are particularly important to data governance specialists and data stewards, who must ensure data quality and reliability. By addressing outliers, these professionals help maintain the robustness of datasets, which is crucial for effective decision-making and strategic planning in organizations.
When analyzing customer purchase data, spotting an outlier like a $10,000 transaction in a sea of $50 purchases can make you question if it’s a VIP customer or just a data entry error.
The term "outlier" was popularized in the 1970s by statistician John Tukey, who used it to describe data points that lie outside the overall pattern of distribution, but it has since become a staple in the lexicon of data science and statistics.
