SQL queries often return technically correct results that are logically incorrect for business reporting. A common issue arises when using JOINs, which can duplicate rows and inflate aggregate sums. Analysts often fail to notice this until data is reconciled with external sources.
The handling of NULL values is a frequent source of errors. Functions like COUNT(column) ignore NULLs, while COUNT(*) includes them, leading to discrepancies. Similarly, comparison operators like NOT IN fail when a NULL is present in the subquery list, whereas NOT EXISTS provides accurate results.
Filtering in LEFT JOINs requires care. Placing conditions on the right-hand table in a WHERE clause effectively turns the join into an INNER JOIN, discarding rows that do not match. Such conditions should instead be placed in the ON clause to preserve the integrity of the left table.
Averages of ratios often produce misleading metrics. Calculating the average of percentages gives equal weight to campaigns of vastly different sizes, whereas dividing the sum of conversions by the sum of clicks provides the true aggregate conversion rate.
Time-based filtering with the BETWEEN operator can lead to data loss. Because it treats the end date as midnight, events occurring later on that day are excluded. Using semi-open intervals with greater-than or equal-to operators is the recommended approach for accurate time-series analysis.
