Context

Missing values are common in analytics tables and operational datasets. Interviewers ask this question to see whether you can distinguish between simply replacing NULLs and making a sound data-cleaning decision.

Core question

Explain how you would handle missing values in a SQL dataset. Your answer should cover:

1. How to identify missing values and understand why they are missing
2. When to leave NULLs as-is versus filling them with defaults or derived values
3. Common SQL techniques for handling missing data in numeric, categorical, and date fields
4. Risks of introducing bias or incorrect business logic when imputing values

Scope guidance

The interviewer is usually looking for a practical, SQL-oriented explanation rather than advanced statistical imputation. Focus on data quality checks, business context, and common PostgreSQL techniques such as COALESCE, CASE WHEN, filtering, and aggregation-based replacement strategies.