SQL lesson
NULL and Three-Valued Logic
Practice NULL and Three-Valued Logic with a worked query, common mistakes, and a checked next step. Reason about missing information with IS NULL and unknown Boolean results.
What you will learn
Reason about missing information with IS NULL and unknown Boolean results.
Before writing SQL, name what one result row should mean for NULL and Three-Valued Logic.
Learning objectives and prerequisites
Each objective names an observable SQL behavior, with prerequisite topics linked in the order you need them.
Plan about 20 minutes for the explanation, worked example, prediction, and first hands-on attempt. Delayed review adds practice later.
- Objective: Reason about missing information with IS NULL and unknown Boolean results.
- Skill focus: null-logic
- Prerequisite: review Text Matching and Normalization before this lesson.
Example query and result
Study the compact worked query first, then open the editor when you are ready to change and run it.
- Starting rows: one candidate row before filtering.
- Result rows: one qualifying row in stable order.
SELECT product_id, product_name FROM products WHERE status = 'active' ORDER BY product_id;| Example output | Meaning |
|---|---|
| 1 | Trail Mix | active row kept by the predicate |
| 4 | Desk Lamp | later active row kept in stable order |
Syntax pattern
Use the syntax pattern as a shape, not as a memorized answer. Replace table, column, condition, grouping, and ordering names according to the stated grain.
SELECT column_name FROM table_name WHERE condition ORDER BY stable_key;| Input grain | Output grain | Validation focus |
|---|---|---|
| one candidate row before filtering | one qualifying row in stable order | Rows, order, duplicates, nulls, and edge cases |
Common mistakes and why they fail
A common mistake is matching the visible rows while ignoring ordering, duplicate policy, null behavior, tie behavior, or the stated result grain.
Why it fails: NULL and Three-Valued Logic checks changed examples, so a query that only copies visible rows can break when row counts, labels, nulls, or ties change.
PostgreSQL dialect notes stay explicit when syntax, date handling, transaction behavior, or comparison semantics matter.
Predict the result before you run it
Before opening the app, predict the output grain, the first column, and one edge case that could change the answer.
This is a reading prompt only; the app opens only when you choose to practice.
- Prediction: name one row that should appear or one row that should be excluded.
- Check: explain whether nulls, duplicates, ties, or missing relationships affect the result.
- Transfer: say what would change on a second dataset with different labels and counts.
Practice and related resources
Move to the app when you want the editor, checks, hints, and solution review.
Page highlights
- Clear task requirements
- Learning objectives and prerequisite links
- Original worked query and readable result example
- What each result row means
- Misconception and edge-case notes
- Predict the result before running the query
- Previous and next lesson navigation