Gaussian RNG
Generate normally distributed random numbers using the Box-Muller transform with custom mean and standard deviation.
Last updated: May 10, 2026
Generator Options
The average value or center of the distribution.
The spread of the bell curve. Higher value = wider spread.
Distribution
Raw Data Output
Gaussian RNG
An advanced statistical utility for generating random numbers that follow a normal (Gaussian) distribution. Utilizing the Box-Muller transform algorithm, this tool allows you to specify a custom mean (μ) and standard deviation (σ). It's an essential online calculator for data scientists, statisticians, and developers needing realistic, normally distributed sample sets for simulations, testing, or statistical modeling. Generate single values or bulk datasets instantly in your browser without any server-side processing. Each value is produced as the mean plus σ times a standard normal sample, so roughly 68% of results land within one standard deviation of the mean and about 95% within two. You can preview the shape of your sample with the built-in histogram, tune the parameters until the spread looks right, then export the full list as CSV for use in R, Python, or Excel.
Why This Tool Exists
What makes this useful — and why I built it this way.
Accurate Modeling: Perfect for simulating real-world phenomena.
Visual Feedback: Built-in histograms let you verify the distribution shape immediately.
Fast & Secure: No data is sent to external servers.
Reproducible Setup: Fix the mean and spread once and regenerate fresh samples as often as you need.
Confidential Friendly: Safe for research or financial figures that are not allowed to leave your machine.
When You'd Use This
Real situations where this tool saves the day.
Data Science: Creating mock datasets for machine learning models.
Game Development: Procedurally generating natural-feeling random events.
Statistics: Teaching or testing normal distribution theories.
Simulation: Feeding normally distributed inputs into Monte Carlo and risk models.
Quality Control: Modeling natural variation in measurements for tolerance and Six Sigma testing.
Using Gaussian RNG
It's straightforward — here's how it works.
Enter your desired Mean (average value).
Enter your desired Standard Deviation (spread of the curve).
Specify how many numbers you wish to generate.
Click 'Generate' to create the dataset and view the histogram distribution.
Copy the results to your clipboard or export them as a CSV file.
Questions People Ask
Honest answers about how this works.
What algorithm does this use?
Great question. This tool uses the Box-Muller transform: pairs of uniform random numbers are mapped through a logarithm and a square root, then combined with sine or cosine, to produce independent standard normal values. Each result is scaled by your standard deviation and shifted by your mean before it is listed.
Can I really generate a large dataset?
Yes, you can generate up to 10,000 numbers in one run, watch them fill the histogram so you can see the bell shape form, then export the whole set as a CSV file with a single click. For larger samples, run the generator again and append the results in your own tool.
Does the generated data leave my browser?
No. Every number is produced by JavaScript running in your own browser and lives only in the page until you copy or download it. Nothing is uploaded to a server or written to storage, so confidential research or financial samples stay private.
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