During initial data analysis, which issue should the analyst flag?

• A. Completeness
• B. Mismatch
• C. Duplication
• D. Outlier

Answer: (D)

Flagging outliers during initial data analysis is essential because extreme values can distort summaries, bias models, and reveal potential data quality issues. These anomalous observations may be actual rare events, measurement errors, or data entry mistakes. Identifying them early lets you verify their validity, decide whether to correct, transform, or use robust methods, and document how you handle them for transparent analysis. While completeness, mismatch, and duplication are important quality checks, outliers directly influence the integrity of initial statistical insights and modeling assumptions.