Don't just calculate your results — engineer them. We treat statistical analysis like production software.
PyStatsV1 is an open-source organization applying modern software engineering
standards to applied statistics.
Our mission is to help students, instructors, and researchers escape the
Reproducibility Crisis by treating statistical analysis not as a scratchpad,
but as a transparent, testable, and re-runnable system.
PyStatsV1 = statistics + software engineering for transparent and reproducible research.
https://github.com/pystatsv1/PyStatsV1
A chapter-based applied statistics toolkit in plain Python.
- For Researchers: “Audit-proof” pipelines with version control, tests, and one-command regeneration.
- For Students: Learn statistics using clean Python scripts that mirror textbook chapters.
- For Instructors: Standardized Makefile workflows and CI smoke tests make grading and replication easy.
| Feature | Benefit |
|---|---|
| Plain Python Scripts | No black boxes. Every step is transparent and reviewable. |
| Makefiles | “One-click verification” — recreate an entire analysis or paper with one command. |
| Synthetic Data | Share complete workflows publicly without risking participant confidentiality. |
| Continuous Integration | A safety net that catches errors the moment they appear. |
- R Users: Line-by-line Python equivalents of classic R textbook examples.
- Practitioners: Prefer scripts and reproducible pipelines over GUI-driven workflows.
- Educators: Classroom-ready case studies and chapter labs designed for teaching.
- Students: Learn both statistics and clean Python used in modern industry.
git clone https://github.com/pystatsv1/PyStatsV1.git
cd PyStatsV1
make psych-ch07 # run a complete Track B chapter lab
We welcome contributors from all backgrounds — psychology, economics, statistics, computer science, or anyone interested in transparent, reproducible research.
You don’t need to be a Python expert to help:
- Fix a typo in the docs
- Improve an explanation in a chapter
- Add a simple test or example
Start by visiting PyStatsV1/PyStatsV1 and reading CONTRIBUTING.md. Open an issue to say hello or propose a new idea — we’re glad you’re here.
