robust statistics examples

In the realm of data analysis, classical statistical methods often rely on ideal assumptions: data are normally distributed, free of errors, and contain no extreme values. However, real-world data rarely conform to these assumptions. Outliers, skewed distributions, and measurement errors can distort results, leading to misleading conclusions. This is where robust statistics come into play—offering … Read more