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title: Random Number Generation
---

In 2023, I wrote a 4,000-word computer science research paper comparing the
pseudorandom number generators used by Python and JavaScript.

[Read the full paper](/research.pdf).

## The question

Python's `random` module uses MT19937, better known as the Mersenne Twister.
JavaScript engines commonly use Xorshift128+ for `Math.random()`. I wanted to
understand how each algorithm worked and which one produced better random
numbers.

## How I tested them

I generated one billion integers with each generator and used a chi-square
goodness-of-fit test to compare their distributions. I then ran both output
streams through PractRand to look for correlations and patterns that the
distribution test could not detect.

## What I found

Both generators passed the chi-square test. Their outputs had similarly uniform
distributions, but PractRand exposed a large difference. Xorshift128+ failed
after 256 megabytes of data, while MT19937 reached 256 gigabytes before
failing.

Xorshift128+ was substantially faster. MT19937 produced statistically stronger
output in my tests. The useful answer depended on the job: speed suited
JavaScript's common browser workloads, while Python's generator offered a
stronger general-purpose balance.