lesson
NULL and Three-Valued Logic
Reason about missing information with IS NULL and unknown Boolean results. This public lesson explains the mental model, syntax pattern, result grain, mistakes, and next practice step for M09.
Practice this lesson
Answer first mental model
Reason about missing information with IS NULL and unknown Boolean results.
Mental model: name the intended output grain for NULL and Three-Valued Logic before writing SQL so later checks can distinguish a correct query from a coincidentally matching result.
Learning objectives and prerequisites
Learning objectives are stated as observable behaviors, and prerequisite links are public pages rather than hidden app state.
Plan about 20 minutes for the public read, worked example, prediction, and first app attempt; delayed review can add more 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.
Original worked query and result grain
The worked query is a compact SQL Trail example that can be read without loading the interactive engine.
Input grain: one candidate row before filtering.
Output grain: 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 | Result comparison, ordering, 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 meaning across deterministic variants, so a query that only copies the 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.
Lightweight public prediction
Before opening the app, predict the output grain, the first column, and one edge case that could change the answer.
This is a public reading prompt only; it does not load PGlite, learner history, drafts, or browser-only state.
- 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, local SQL worker, variant checks, hints, and Solution Studio comparison.