Your question is Analyzing Missing Values in Clinical Data. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
Missing values are common in clinical and device-related datasets, and how you handle them can materially change downstream conclusions. In an interview, the goal is usually not just to say “drop nulls,” but to show a structured SQL-first approach.
You are given procedure data from KARL STORZ systems where some fields are missing, such as device model, procedure duration, and site information. Explain how you would proceed with your analysis using SQL. Describe how you would first quantify missingness, check whether it is concentrated in specific hospitals, products, or time periods, and decide when to exclude rows, impute values, or keep missingness as its own analytical category.
Focus on a practical analysis workflow rather than statistical theory alone. The interviewer expects you to discuss null handling, validation checks, the role of joins in introducing missing values, and how you would communicate the impact of your choices on final metrics.