Robust Statistics for Data Scientists Part 2: Resilient Measures of Relationships Between Variables

Grasping the interconnections among variables is essential for making data-driven decisions. When we accurately evaluate these links, we bolster the trustworthiness and legitimacy of our findings, crucial in both scholarly and practical contexts. Data scientists frequently turn to Pearson’s correlation and linear regression to probe and measure variable relationships. These methods presume data normality, independence, and consistent spread (or homoscedasticity) and perform well when these conditions are met. However, real-world data scenarios are seldom ideal. They’re typically marred by noise and outliers, which can skew the results of traditional statistical techniques, leading to incorrect conclusions. This piece, the second in our series on robust statistics, seeks to navigate these obstacles by delving into robust alternatives that promote more dependable insights, even amidst data irregularities. <a href="https://towardsdatascience.com/robust-statistics-for-data-scientists-part-2-resilient-measures-of-relationships-between-variables-a59b37a6907f">Read More</a>