An interesting thing about humans is that they are not good random number generators.
If you ask a person to "pick a random number between 1 and 100", they are remarkably predictable. Answers cluster on 37 and 73, on "messy" numbers, and on memes like 42 and 69, while round numbers are quietly avoided. A true random generator would instead produce a flat, uniform distribution.

This project asks gpt-4.1 the same question 10,000 times and characterizes the distribution it produces, measured against a uniform baseline. Does an LLM, which is trained on human text, behave like a fair die, or does it inherit the lumpy human pattern?

Full design and methodology: docs/LLM Random Bias Experiment SDD.md.

This experiment is an LLM-focused follow-up to two well-known explorations of human number-picking bias.

Full experimental design is in the SDD; the essentials:

• Model. gpt-4.1 (OpenAI), called via the Responses API. It is a non-reasoning model. It emits a direct answer rather than deliberating; what we're measuring is its raw output distribution, not a reasoning strategy. The exact model string is recorded in every raw-CSV row (Model column) and in data/raw/run_metadata.json, so the dataset is self-describing.
• Sample size. N = 10,000 independent calls — enough for a chi-square goodness-of-fit test and per-number proportions stable to ~±0.5 pp.
• Sampling. temperature = 1.0, so the model exercises its full sampling distribution. This is the experiment: at low temperature it would just repeat one number.
• Prompt. A fixed system prompt instructs the model to output only one integer between 1 and 100; the user prompt requests the number and carries a unique uuid4. (The UUID is request-tracing hygiene, not cache-busting — at temperature 1.0 every call should sample independently regardless.)
• Baseline. The result is compared against a uniform distribution — what a fair generator would produce — not against human data (see Assumptions).
• Pipeline. Four stages — collect → clean → transform → stats, detailed below. Cleaning validates every answer is an integer in [1, 100] and reports the rejection rate.

This is an illustrative probe, not a definitive study. Key caveats — see the SDD's Limitations section for the formal treatment:

• Single model. Results describe gpt-4.1 only and do not generalize to other models or providers.
• "Randomness" is a sampling artifact. The model is not a random number generator; it samples a learned token distribution. We characterize that distribution — we do not claim the model is trying to be random.
• Prompt- and temperature-dependent. A different prompt wording or sampling temperature could shift the distribution. Both are fixed and documented.
• Not "ChatGPT the product." This tests a model through the API at a fixed temperature — not the consumer ChatGPT app, which adds routing, tools, and a system prompt outside our control.

gpt-4.1 is emphatically not a uniform random generator. A chi-square goodness-of-fit test against a uniform distribution (N = 10,000, df = 99) returns χ² = 15,604, p ≈ 0 — the deviation is so large it underflows any significance threshold. Asked for a random number, the model produces a lumpy, distinctly human-shaped distribution.

The five most-picked numbers overall — 47, 57, 72, 37, 42 — lean heavily on numbers ending in 7 (three of the five), the same "number that feels random" pull seen in humans.

All multiples of 10, except for 10 itself, were picked exactly 0 times in 10,000 calls. 10 was picked exactly once. Humans avoid round numbers — gpt-4.1 essentially refuses them.

One number breaks the human pattern. 69 is a meme number humans over-pick. gpt-4.1 under-picks it (0.29× expected: ~29 occurrences against ~100). The model inherited the "smart" meme (42) and not the crude one. Our hypothesis is that this is a product of safety guardrails during pre-training and post-training. It is the most interesting aspect in the dataset: the model's bias is not a raw copy of human bias but a moderated version of it.

The hypothesis holds. An LLM trained on human text, asked to be random, reproduces human random-number bias: the pull toward 37 and 73, the meme spike at 42, the aversion to round numbers — with one guardrail-likely exception. The interactive distribution chart shows the full 1–100 shape.

All figures from data/processed/stats_summary.csv.

collect → clean → transform → stats. Each stage reads the previous stage's committed CSV, so any stage can be re-run on its own.

This project uses uv for everything.

The raw dataset is committed to this repo, so you can reproduce the entire analysis without spending a cent:

uv run python -m llm_random_bias.clean
uv run python -m llm_random_bias.transform
uv run python -m llm_random_bias.stats

cp .env.example .env          # then edit .env and add your OPENAI_API_KEY
uv run python -m llm_random_bias.collect
# then run clean / transform / stats as in Path 1

Cost & runtime: ~10,000 short calls to gpt-4.1 cost roughly US$2 and finish in a few minutes at the default concurrency. The collector refuses to overwrite an existing raw CSV — delete it first to re-collect.

The distribution bar chart is built in Exmergo Viz (our AI dashboard agent) directly from data/processed/distribution.csv. The fully interactive data viz can be viewed here.

uv run ruff check .
uv run ruff format .
uv run mypy src
uv run pytest

See CONTRIBUTING.md.

MIT — see LICENSE.