How do you make sure your analysis is correct before presenting it?

Show a checking habit, because a confident wrong number is worse than no number.

• Sanity-check the totals against something independent. If your query says 4,200 orders last month, does the finance report agree? Reconciling against a source people already trust catches most errors immediately.
• Check the row count after every join. A join that silently multiplies rows is the single most common cause of inflated numbers in SQL analysis, and it produces results that look plausible.
• Look at the raw data, not just the aggregate. Averages hide duplicates, nulls, test records, and outliers. Spot-check individual rows.
• Question a surprising result before celebrating it. An unexpectedly strong finding is more often a bug than a discovery.
• Have someone else review the logic, particularly the filters and date ranges, which is where assumptions hide.
• Document your assumptions — which date field, which definition of "active", what was excluded — so a reviewer can challenge them.

Note: Being willing to say "I checked and my first number was wrong" is what earns trust over time. Analysts who never report an error are usually not checking.