What is the difference between min-max normalization and z-score standardization?

Min-max scales all values into a fixed range (default 0–1), preserving the shape of the distribution. Z-score expresses each value as the number of standard deviations from the mean; the result has mean=0 and std=1 but is not bounded to a fixed range.

When should I use min-max vs z-score?

Use min-max when your algorithm requires values in a specific range (e.g. neural network inputs). Use z-score when you need to compare values across distributions with different scales, or when your algorithm assumes normally distributed input.

What happens if all values are the same?

If all values are identical, min-max normalization is undefined (division by zero). Z-score is also undefined because the standard deviation is 0. The tool displays a clear warning in this case.

Can I use a custom range for min-max normalization?

Yes. Enter your desired minimum and maximum in the Custom Range fields. Any range is valid as long as min < max. The formula used is: a + (x − data_min) × (b − a) / (data_max − data_min), where [a, b] is your target range.

How many numbers can I normalize at once?

The tool runs entirely in your browser and can handle thousands of values. For very large datasets, consider a dedicated data science library.

Normalization Calculator

Paste a list of numbers and instantly get min-max normalized values and z-scores in a comparison table.

Input Data

Numbers (comma or newline separated)

Method

Both Min-Max Z-Score

Min-Max Output Range

Enter numbers on the left and click Calculate.

Results

Summary

Normalize a dataset using min-max normalization (scales values to [0,1] or a custom range) or z-score standardization (mean=0, std=1). Paste or type your numbers, pick a method, and get a full results table with summary statistics.

How it works

Paste or type your numbers into the input box, separated by commas or newlines.
Choose Min-Max Normalization or Z-Score Standardization (or view both).
For min-max, optionally set a custom output range instead of the default [0, 1].
Click Calculate to see a table with original, min-max, and z-score values for each number.
Copy the normalized values as a comma-separated list for use in spreadsheets or code.

Use cases

Preprocess features before training a machine learning model.
Compare measurements from different scales on the same chart.
Standardize survey response scores across multiple questions.
Scale sensor readings to a common range for data analysis.
Normalize price or revenue data before statistical comparison.

Frequently Asked Questions

Last updated: 2026-06-15 · Reviewed by Nham Vu