Validating SQL output and aggregating health data

The first question is a verification question, not a programming one, and answers that drift into SQL syntax miss it. The issue is that a query can run cleanly, return plausible-looking rows, and still be wrong — a join that silently duplicates rows, a filter that excludes nulls you meant to keep, a date range off by a day at a boundary. So the answer is a set of checks that would catch a wrong result: reconciling totals against an independent source such as a billing or census report, running the same question two different ways and comparing, checking row counts before and after each join, profiling for impossible values and unexpected nulls, and having a second analyst review the logic. Naming referential integrity, range constraints and validation rules gives the answer the vocabulary the course is looking for, and it distinguishes a verification plan from a general intention to be careful.

The raw-versus-summary half has a real answer too. Raw data is preferable when you need to re-cut the analysis along a dimension the summary did not anticipate, when you have to investigate outliers, or when you need to risk-adjust — because a summary has already made choices you cannot undo. Summaries are preferable when the audience needs a decision rather than a dataset, when volume makes transfer impractical, or when minimum cell sizes protect patient privacy, which is a genuine constraint in health data rather than a technicality. On presentation, match the form to the question: tables for exact values someone will look up, charts for trends and comparisons, narrative for the interpretation neither can carry. The second discussion question then asks why aggregation matters at all, and the honest answer is that individual records answer questions about one patient while aggregates answer questions about care. Naming that difference is the whole of the second question.