SQL NULL Comparisons Returning Wrong Results: IS NULL vs = NULL Traps

SQL is built around logic.

Most developers expect comparisons to behave similarly to programming languages.

For example:

age = 25

or

status = 'ACTIVE'

These comparisons are straightforward.

Problems begin when databases contain missing values.

Almost every production database stores NULL values.

Examples include:

• Missing phone numbers
• Unknown birth dates
• Optional addresses
• Pending payments
• Unassigned employees
• Incomplete customer profiles

Suppose you write:

SELECT *
FROM customers
WHERE phone = NULL;

Logically, you might expect:

Rows
Without
Phone Numbers

Instead,

the query returns:

Zero Rows

No syntax error.

No warning.

No exception.

The query executes successfully—

but the results are wrong.

Many developers spend hours debugging joins, indexes, and data imports before discovering that the real issue is SQL's handling of NULL.

Unlike ordinary values,

NULL is not a value.

It represents:

Unknown

Understanding this distinction is essential for writing correct SQL queries.

What You Will Learn From This Article

After reading this guide, you'll understand:

• What NULL represents.
• Why = NULL never works.
• SQL's three-valued logic.
• Correct use of IS NULL.
• Common NULL-related mistakes.
• NULL handling in joins.
• Best practices for production databases.

What Does NULL Mean?

NULL does not mean:

• Empty string
• Zero
• False
• Blank

Instead, it means:

Unknown

or

Missing

The database simply does not know the value.

Why = NULL Doesn't Work

Many developers write:

WHERE email = NULL

SQL evaluates this as:

Unknown
=
Unknown

The answer is:

Unknown

not:

True

Rows are returned only when a condition evaluates to:

TRUE

Unknown values are filtered out.

SQL Uses Three-Valued Logic

Unlike most programming languages,

SQL evaluates expressions as:

TRUE

FALSE

UNKNOWN

This is called:

Three-Valued Logic

NULL introduces the third state.

Correct Way to Check NULL

Instead of:

WHERE phone = NULL

use:

WHERE phone IS NULL;

Similarly,

instead of:

WHERE phone != NULL

write:

WHERE phone IS NOT NULL;

These operators are specifically designed for NULL comparisons.

Common Cause #1

Using = NULL

Example:

SELECT *
FROM employees
WHERE manager_id = NULL;

Result:

No Rows

Solution

Use:

WHERE manager_id IS NULL;

Common Cause #2

Using != NULL

Developers often write:

salary != NULL

Again,

every comparison becomes:

UNKNOWN

Solution

Use:

salary IS NOT NULL

Common Cause #3

NULL Inside Expressions

Example:

price + discount

If:

discount
=
NULL

then:

Result
=
NULL

Arithmetic involving NULL generally produces NULL.

Solution

Use functions such as:

COALESCE(discount, 0)

to substitute default values when appropriate.

Common Cause #4

NULL in WHERE Clauses

Suppose:

WHERE score > 80

Rows with:

score = NULL

are excluded because:

NULL > 80

evaluates to:

UNKNOWN

Common Cause #5

NULL in Joins

Example:

ON orders.customer_id =
customers.id

Rows where:

customer_id
=
NULL

cannot match any record.

Understanding this behavior is important when designing optional relationships.

Using COALESCE

COALESCE() returns the first non-NULL value.

Example:

SELECT
COALESCE(phone, 'Not Available')

Useful for:

• Reports
• Dashboards
• Export files
• Calculated fields

NULL and Aggregate Functions

Most aggregate functions ignore NULL values.

Examples:

COUNT(column)
AVG(column)
SUM(column)

NULL rows are typically excluded from these calculations.

However:

COUNT(*)

counts every row, including those containing NULL values in individual columns.

Understanding this distinction prevents inaccurate reporting.

NULL Ordering

Database systems differ in how NULL values are sorted.

Some place NULL values:

• First
• Last

depending on:

• Database engine
• Sort direction
• Query options

When ordering results,

explicitly specify NULL handling if your database supports it.

NULL and Unique Constraints

Many developers assume:

NULL
=
NULL

For unique indexes,

many database engines allow multiple NULL values because NULL represents "unknown" rather than an actual value.

Always verify behavior for your specific database platform.

Testing for Missing Data

Good reporting queries often include:

WHERE
email IS NULL

or

WHERE
last_login IS NOT NULL

Explicit NULL handling produces predictable results.

Real-World Example

A customer support dashboard displays:

Customers
Without Email

The original query:

WHERE email = NULL

returns:

0 Customers

Support believes every customer has an email address.

In reality,

thousands of records contain NULL values.

Changing the query to:

WHERE email IS NULL

immediately reveals the missing data.

The issue was not the database—it was the comparison operator.

Performance Considerations

Checking NULL values correctly is not only about correctness.

Well-designed indexes and query plans can efficiently process:

IS NULL

and

IS NOT NULL

depending on the database engine and indexing strategy.

Always review execution plans when optimizing large datasets.

Best Practices Checklist

When working with NULL values:

✅ Use IS NULL

✅ Use IS NOT NULL

✅ Learn SQL's three-valued logic

✅ Handle NULL values explicitly

✅ Use COALESCE() when appropriate

✅ Validate aggregate calculations

✅ Test JOIN behavior with NULL values

✅ Understand database-specific NULL ordering

✅ Review execution plans

✅ Test queries using production-like data

Common Mistakes to Avoid

Avoid:

❌ Using = NULL

❌ Using != NULL

❌ Assuming NULL equals an empty string

❌ Ignoring NULL in calculations

❌ Forgetting NULL values in joins

❌ Misinterpreting aggregate results

❌ Assuming every database handles NULL identically

Why This Bug Is Difficult to Diagnose

NULL comparison bugs are particularly deceptive because SQL treats them as perfectly valid syntax. Queries execute successfully without generating errors, yet conditions involving = NULL or != NULL always evaluate to UNKNOWN, causing rows to be filtered out silently. Since no exception is raised, developers often investigate indexes, joins, or data quality before realizing that the comparison itself is incorrect.

Adding to the confusion, NULL behaves differently from ordinary values in arithmetic expressions, aggregate functions, sorting, and joins. Understanding SQL's three-valued logic is therefore essential for diagnosing these seemingly mysterious query results.

Wrapping Summary

NULL is one of SQL's most misunderstood concepts because it represents the absence of a known value rather than a value itself. As a result, comparisons using = NULL or != NULL never evaluate to TRUE, causing queries to return incomplete or empty result sets without producing any errors. Correct NULL handling requires using IS NULL and IS NOT NULL, along with functions such as COALESCE() when default values are needed.

By understanding SQL's three-valued logic, validating how NULL affects joins and aggregate functions, and testing queries with realistic datasets, developers can eliminate one of the most common sources of silent database bugs. Proper NULL handling leads to more accurate reports, reliable business logic, and SQL queries that behave predictably across production environments.