How a joke from a sci-fi novel quietly became a data science tradition.

If you’ve spent any time in data science, you’ve probably seen this:

random_state=42

And eventually, you ask:

Why 42?

Is it mathematically special? Is it statistically superior? Does 42 somehow make your model smarter?

No.

It’s a joke that became a convention.

The Story Behind `42`

In Douglas Adams’ The Hitchhiker’s Guide to the Galaxy, a supercomputer called Deep Thought is asked to find the Answer to the Ultimate Question of Life, the Universe, and Everything.

After 7.5 million years of computation, Deep Thought finally delivers the answer:

42

The problem?

Nobody actually knows what the question was.

Adams chose 42 simply because it was an ordinary, funny number. There was no secret mathematical meaning behind it.

And programmers loved it.

From Sci-Fi to `random_state=42`

Computers often use pseudo-random numbers. They aren’t truly random; they’re generated from an initial value called a seed.

For example:

import numpy as np
np.random.seed(42)
print(np.random.rand(5))

Use the same seed, and you’ll get the same sequence.

That’s incredibly useful in data science because randomness appears everywhere: dataset splitting, shuffling, model initialization, sampling, and more.

Without a fixed seed:

Experiment A → 91.2%
Experiment B → 89.8%
Experiment C → 90.6%

With a fixed seed, your experiment becomes reproducible.

Except That It Mostly Doesn’t

This is the part the random_state=42 habit quietly hides, and it is worth knowing before you trust a number you can reproduce.

A seed fixes one generator. A real training run has several, and they do not share it:

import random, numpy as np, torch

random.seed(42)                  # Python's own RNG
np.random.seed(42)               # NumPy
torch.manual_seed(42)            # PyTorch CPU
torch.cuda.manual_seed_all(42)   # every GPU

Miss one and something is still rolling its own dice. DataLoader workers are the classic escape — each worker process needs its seed set, or your augmentations differ run to run no matter what you wrote at the top of the file.

Then there are the things a seed cannot reach at all:

GPU kernels. cuDNN picks algorithms by benchmarking, and floating-point addition is not associative, so the same maths in a different order gives a slightly different number. torch.use_deterministic_algorithms(True) buys most of it back, and costs you speed.
Thread and worker counts. Change the number of workers and the order things are reduced in changes with it.
Hash ordering. Anything that iterates a set inherits PYTHONHASHSEED, which is randomised per process by default — and it is an environment variable, not something random_state can touch.
Library versions. The same seed into a different implementation is a different sequence. Seeds are not portable across upgrades, and nothing warns you.

So random_state=42 does not make a run reproducible. It removes one source of variation, which is genuinely worth doing, and leaves the rest for you to notice.

So Why `42`?

Because it doesn’t matter.

You could use:

random_state=7

or:

random_state=123

or:

random_state=2026

They can all work.

42 simply became popular because programmers enjoy the Adams reference.

It’s basically a nerdy handshake:

“I know why you chose 42.”

The Trap Nobody Admits To

There is a worse way to use a seed than forgetting it, and that is choosing it.

You run the split, the score is 89.1%. You try another seed, 91.4%. You keep that one.

Nothing in your code looks wrong afterwards. The notebook is clean, the number is real, and the experiment is perfectly reproducible — it reproduces the luckiest split you happened to find. You have tuned a hyperparameter that is supposed to be noise, and you have done it on your test set.

The fix is unglamorous. Run several seeds and report the spread:

scores = [run(seed) for seed in range(10)]
print(f"{np.mean(scores):.3f} ± {np.std(scores):.3f}")

If that spread is wide, the single number you were about to put in a slide was never a result. It was a draw. And knowing the width of the distribution is more honest than any point estimate from inside it.

The Best Part

There’s a beautiful irony here.

In The Hitchhiker’s Guide to the Galaxy, 42 is the answer without the question.

In data science, you can have a perfectly reproducible result without answering the right question.

Setting:

random_state=42

makes your experiment repeatable.

It doesn’t make your experiment correct.

It doesn’t make your model better.

And it definitely doesn’t make your analysis meaningful.

So the real lesson of 42 might be:

Before celebrating the answer, make sure you know the question.

And yes, 42 is still the answer to life, the universe, and everything.

We’re just waiting for the question.

Don’t forget your towel.

The Story Behind 42