What can go wrong when filtering SQL rows that contain NULL values?

Explain SQL null semantics in filters and joins

Explains SQL three-valued logic, null-safe filters, optional relationships, and API behavior around missing values.

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In SQL, NULL means unknown or missing, so comparisons like column = NULL do not work the way normal equality does. I would use IS NULL or IS NOT NULL for explicit null checks. I would also remember that conditions involving NULL can evaluate to unknown, which means the row does not pass a WHERE filter. This matters in optional fields, left joins, and search filters.

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NULL comparison behavior, three-valued logic, optional data, joins, indexes, and API-facing defaults.

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NULL behavior is a quiet source of interview mistakes and real production bugs. A candidate who handles it well usually understands SQL as a different model from normal application booleans.
In SQL, NULL means unknown or missing, so comparisons like column = NULL do not work the way normal equality does. I would use IS NULL or IS NOT NULL for explicit null checks. I would also remember that conditions involving NULL can evaluate to unknown, which means the row does not pass a WHERE filter. This matters in optional fields, left joins, and search filters.
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I would design filters so missing data has a clear product meaning. For example, if status can be NULL, I would decide whether NULL means draft, unknown, imported legacy data, or not applicable. In left joins, filtering a related table column in WHERE can accidentally turn the query into inner-join behavior, so I would place conditions carefully. For APIs, I would map NULL to a deliberate response shape rather than relying on accidental defaults.

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Writing column = NULL instead of IS NULL.
Filtering a left-joined table in WHERE and losing unmatched rows.
Using NULL for several different business meanings.
Letting API behavior depend on accidental database defaults.

What can go wrong when filtering SQL rows that contain NULL values?

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