My first instinct was Pearson and I almost just said it out loud before catching myself.
Start by acknowledging that outliers can distort Pearson correlation, then discuss robust alternatives like Spearman or Kendall rank correlations, or robust covariance methods. Emphasize that the choice depends on the nature of the outliers and the underlying data distribution, and that you would validate with visualizations and sensitivity analysis.
Pro tip: Mention that you would first investigate whether outliers are errors or genuine extreme values, as this determines whether to remove, transform, or use robust methods. Also, highlight that you would communicate the impact of outliers on correlation to stakeholders to ensure informed decisions.
Detect outliers using visualization (boxplots, scatterplots) and statistical methods (z-scores, IQR). Determine if they are data errors or legitimate extreme values.
Select appropriate methods: Spearman's rank correlation, Kendall's tau, or robust covariance estimators (e.g., MCD, M-estimators) that are less sensitive to outliers.
Apply transformations like log or winsorization to reduce outlier impact if the relationship is monotonic but not linear, or if outliers are extreme but valid.
Compute correlation with and without outliers, and using different methods. Assess stability and interpret differences in context of the business problem.
Explain the chosen method, its assumptions, and the impact of outliers on the correlation. Discuss implications for decision-making and potential next steps.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.