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technologyreview.com·2h ago
Data Science
Towards Data Science·27d ago·5 min read
Inside the Subspace Where Spurious Correlations Are Born
TL;DR
Small sample sizes can lead to misleadingly high correlations purely by chance, while larger datasets do not guarantee meaningful relationships. The article explores the mathematical foundations of spurious correlations and their implications in data analysis.
✦ Why It Matters
Today, engineers should critically evaluate correlation results in their datasets to avoid misleading conclusions in their analyses.
Key Takeaways
- Evaluate sample size before interpreting correlation results.
- Understand that small studies can yield extreme correlations by chance.
- Use the exact distribution of Pearson's correlation for better accuracy.
- Be cautious of interpreting correlations in high-dimensional datasets.
How It Works
The article explains that centering moves data onto a hyperplane, while normalization constrains it to a unit sphere. This geometric understanding helps clarify how sample correlations are derived and why they can appear significant even when variables are independent.