Description: Outlier filtering is a crucial process in data analysis that involves identifying and removing data points that significantly deviate from the general trend of the dataset. These outliers, or anomalies, can distort the results of statistical analyses, affecting the accuracy of predictive models and data interpretation. By eliminating these extreme data points, the aim is to improve the quality of the analysis, allowing conclusions to be more representative of the overall behavior of the dataset. This process not only helps achieve more accurate results but also facilitates the identification of patterns and trends that might go unnoticed in the presence of outliers. In the context of data analysis, outlier filtering becomes an essential tool to ensure data integrity and the validity of analyses performed. The proper application of filtering techniques can lead to better decision-making in various fields, from scientific research to business analysis and artificial intelligence.
