What is the syntax for STANDARDIZE in Excel?

The syntax for STANDARDIZE is: =STANDARDIZE(x, mean, standard_dev)

What is STANDARDIZE used for?

STANDARDIZE converts a value to a z-score, showing how many standard deviations it is from the mean. Formula: z = (x - mean) / stdev. This allows comparing values from different distributions or scales on a common basis.

How do I interpret z-scores?

Z=0 means the value equals the mean. Z=1 means one standard deviation above mean. Z=-2 means two standard deviations below. About 68% of data falls within z=±1, 95% within z=±2, 99.7% within z=±3 (for normal distributions).

When should I standardize data?

Standardize when: comparing scores from different tests (SAT vs ACT), combining variables with different units, preparing data for certain statistical analyses, or identifying outliers. A z-score beyond ±3 often indicates an outlier.

Statistical

STANDARDIZE Function in Excel

Returns a normalized value (z-score) from a distribution.

Syntax

=STANDARDIZE(x, mean, standard_dev)

Arguments

x (required): Value to normalize
mean (required): Distribution mean
standard_dev (required): Standard deviation

Examples

=STANDARDIZE(42, 40, 1.5) - Calculate z-score - Result: 1.333

STANDARDIZE for data analysis

Confirm whether you need entire columns, filtered subsets, or distinct values.
Use [COUNTIFS](/functions/countifs/) and [SUMIFS](/functions/sumifs/) for multi-criteria metrics.
Pivot tables complement single-cell statistical formulas for exploration.

Common errors

#NUM! if standard_dev <= 0

Use cases

Z-score calculation
Data normalization
Comparison across scales

Frequently asked questions

What is STANDARDIZE used for? STANDARDIZE converts a value to a z-score, showing how many standard deviations it is from the mean. Formula: z = (x - mean) / stdev. This allows comparing values from different distributions or scales on a common basis.
How do I interpret z-scores? Z=0 means the value equals the mean. Z=1 means one standard deviation above mean. Z=-2 means two standard deviations below. About 68% of data falls within z=±1, 95% within z=±2, 99.7% within z=±3 (for normal distributions).
When should I standardize data? Standardize when: comparing scores from different tests (SAT vs ACT), combining variables with different units, preparing data for certain statistical analyses, or identifying outliers. A z-score beyond ±3 often indicates an outlier.

Editorial review

Reviewed by Excel.Directory Editorial Team. Updated May 2026.