How to Use randn in MATLAB: Syntax and Examples

In MATLAB, use randn to generate random numbers from a standard normal distribution (mean 0, variance 1). You can specify the size of the output matrix by passing dimensions as arguments, like randn(3,4) for a 3-by-4 matrix of random values.

Syntax

The randn function generates random numbers from a normal distribution with mean 0 and standard deviation 1.

• r = randn: Returns a single random number.
• r = randn(n): Returns an n-by-n matrix.
• r = randn(m,n): Returns an m-by-n matrix.
• r = randn(sz): Returns an array with size specified by vector sz.

matlab

r = randn;
r = randn(3);
r = randn(2,4);
r = randn([2,3,4]);

Output

r = 0.5377

r =
   -1.2075    0.2773    1.0845
    0.2779    0.5469   -0.5643
   -0.5469   -0.4634    0.2410

r =
    0.5377   -1.2075    0.2773   1.0845
    0.2779    0.5469   -0.5643  -0.5469

r = randn array of size 2x3x4 with random values

Example

This example creates a 3-by-3 matrix of random numbers from the standard normal distribution and calculates its mean and standard deviation.

matlab

A = randn(3,3);
meanA = mean(A, 'all');
stdA = std(A, 0, 'all');
[A, meanA, stdA]

Output

[
  -0.8147   0.9058   0.1270
   0.9134   0.6324   0.0975
   0.2785   0.5469   0.9575
]
meanA = 0.2204
stdA = 0.5837

Common Pitfalls

Common mistakes when using randn include:

• Confusing randn with rand. randn generates normal distribution values, rand generates uniform distribution values.
• Not specifying size arguments correctly, which can cause unexpected output shapes.
• Assuming randn output is always positive; it can be negative since it is normally distributed.

matlab

wrong = randn;
right = randn(1,1);

Output

wrong = 0.5377
right = 0.5377

Quick Reference

Usage	Description
randn	Single random number from standard normal distribution
randn(n)	n-by-n matrix of random numbers
randn(m,n)	m-by-n matrix of random numbers
randn(sz)	Array with size specified by vector sz

Key Takeaways

Use randn to generate random numbers from a normal distribution with mean 0 and variance 1.

Specify dimensions as arguments to control the size of the output matrix or array.

randn can produce negative and positive values because it follows a normal distribution.

Do not confuse randn with rand; rand generates uniform random numbers.

Use mean and std functions to analyze the generated random data.