jvns/stats-fun — explained in plain English
Analysis updated 2026-08-08 · repo last pushed 2022-04-13
Run notebook examples to see how random sampling works visually.
Tweak sample sizes and parameters to build intuition about statistical outcomes.
Explore how different data distributions behave by running the code yourself.
| jvns/stats-fun | birdsarah/gtimelog-viz | mytechnotalent/simple-rag-agent | |
|---|---|---|---|
| Stars | 8 | 7 | 7 |
| Language | Jupyter Notebook | Jupyter Notebook | Jupyter Notebook |
| Last pushed | 2022-04-13 | 2016-09-09 | 2024-12-08 |
| Maintenance | Dormant | Dormant | Stale |
| Setup difficulty | easy | easy | moderate |
| Complexity | 1/5 | 2/5 | 3/5 |
| Audience | general | general | developer |
Figures from each repo's GitHub metadata at analysis time.
No README or setup instructions, you need to open the notebooks directly in Jupyter to explore them.
The jvns/stats-fun repository is a small collection of Jupyter notebooks exploring statistics concepts in a hands-on, visual way. It is less a polished tool and more a learning sandbox, where the author works through statistical ideas using code and charts rather than abstract formulas. The notebooks cover topics like random sampling, how sample sizes affect results, and what happens when you generate data from different distributions. The approach is practical: instead of just reading about a concept, you can run the code, see the outputs, and tweak parameters to build intuition. The interactive format makes it easy to experiment and see how changing inputs shifts the results. This would appeal to anyone trying to get a more intuitive grasp of statistics, whether you are a student, a data-curious PM, or a developer who occasionally encounters statistical claims and wants to understand them better. The concrete examples, like simulating repeated random draws and comparing outcomes, make abstract ideas feel tangible. The README itself does not include any description or setup instructions, so you will need to open the notebooks directly to see what each one covers. Given the minimal documentation, this is best suited to someone comfortable enough with Jupyter notebooks to explore on their own.
A small collection of Jupyter notebooks that explore statistics concepts hands-on with code and charts, helping you build intuition by running and tweaking examples yourself.
Mainly Jupyter Notebook. The stack also includes Jupyter Notebook, Python.
Dormant — no commits in 2+ years (last push 2022-04-13).
No license information is provided in this repository.
Setup difficulty is rated easy, with roughly 5min to a first successful run.
Mainly general.
This repo across BitVibe Labs
Verify against the repo before relying on details.