Robust Statistics for Data Scientists Part 1: Resilient Measures of Central Tendency and Dispersions

While classical statistics have served us well, their susceptibility to outliers and extreme values can lead to misleading conclusions. Enter robust statistics, which aims to provide more reliable results under a wider variety of conditions. This approach is not about discarding outliers without consideration but about developing methods that are less sensitive to them. Robust statistics is grounded in the principle of resilience. It’s about constructing statistical methods that remain unaffected, or minimally affected, by small deviations from assumptions that traditional methods hold dear. This resilience is crucial in real-world data analysis, where perfectly distributed datasets are the exception, not the norm. <a href="https://towardsdatascience.com/robust-statistics-for-data-scientists-part-1-resilient-measures-of-central-tendency-and-67e5a60b8bf1">Website</a>