Why Over-Reliance on Pearson Correlation Is a Flawed Approach in Modern Data Science
By
Valeriy Manokhin, PhD, MBA, CQF
Summary
This article criticizes the over-reliance on Pearson correlation coefficients in data science projects. It argues that treating correlation matrices as definitive insights is outdated and compares it to using a 19th-century statistical tool in the age of AI. The piece highlights how teams ritualistically calculate correlations, create heatmaps, and make decisions based on this limited metric, ignoring its significant flaws.
Source
Twitter / XWhy Over-Reliance on Pearson Correlation Is a Flawed Approach in Modern Data Sciencevaleman.medium.comKey quotes
· 3 pulledThe correlation coefficient has more holes than Swiss cheese.
Red means strong. Blue means weak. Numbers close to 1 are celebrated. Numbers close to 0 are ignored.
And just like that, a 19th-century statistical tool becomes the foundation of modern decision-making.
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