Kendall Correlation vs Point Biserial Correlation

Developers should learn Kendall correlation when working with data that is ordinal, has ties, or contains outliers, such as in ranking systems, survey responses, or non-normal datasets meets developers should learn point biserial correlation when working with datasets that include binary outcomes, such as a/b testing results, classification tasks, or survey data with yes/no responses. Here's our take.

Kendall Correlation

Nice Pick

Developers should learn Kendall correlation when working with data that is ordinal, has ties, or contains outliers, such as in ranking systems, survey responses, or non-normal datasets

Pros

• It is particularly useful in machine learning for feature selection, evaluating model performance on ranked outputs, and in data analysis tasks where monotonic relationships need to be quantified without parametric assumptions
• Related to: statistics, data-analysis

Cons

• Specific tradeoffs depend on your use case

Point Biserial Correlation

Developers should learn point biserial correlation when working with datasets that include binary outcomes, such as A/B testing results, classification tasks, or survey data with yes/no responses

Pros

• It is useful for feature selection in machine learning to identify which continuous features correlate strongly with binary targets, and in data analysis to validate hypotheses about group differences based on continuous measures
• Related to: statistics, data-analysis

Cons

• Specific tradeoffs depend on your use case

The Verdict

Use Kendall Correlation if: You want it is particularly useful in machine learning for feature selection, evaluating model performance on ranked outputs, and in data analysis tasks where monotonic relationships need to be quantified without parametric assumptions and can live with specific tradeoffs depend on your use case.

Use Point Biserial Correlation if: You prioritize it is useful for feature selection in machine learning to identify which continuous features correlate strongly with binary targets, and in data analysis to validate hypotheses about group differences based on continuous measures over what Kendall Correlation offers.

The Bottom Line

Kendall Correlation wins

Developers should learn Kendall correlation when working with data that is ordinal, has ties, or contains outliers, such as in ranking systems, survey responses, or non-normal datasets