Intuitive Biostatistics with Python

Official Companion Code, Datasets, and Reader Community

📖 Access Book via Oxford Academic (DOI) • 🛒 Order Print / E-Book (OUP) • 💬 Reader Community

📖 Get 30% Off the Book: Intuitive Biostatistics with Python: A Python Companion Guide for Life Sciences Professionals (Oxford University Press, 816 Pages, ISBN: 9780197845028) is out now! Use promotion code AUFLY30 at global.oup.com/academic to save 30% (£75.25 / $114.10).

---

📖 A Note to the Reader (Preface)

While you can clone this repository and run the scripts directly, I highly recommend typing the code out yourself as you read. The best way to build an intuitive understanding of biostatistics is through hands-on experimentation.

Use this repository as a safety net to check your work, but do not hesitate to break the code, modify parameters, and most importantly, apply these scripts to your own unique datasets!

---

📑 Interactive Notebook Directory

All Jupyter Notebooks correspond directly to the book chapters and can be browsed directly on GitHub or run locally:

Chapter	Topic	Notebook
01	Quantifying scatter of continuous data	01_quantifying_scatter_continuous_data.ipynb
02	Gaussian distribution	02_gaussian_distribution.ipynb
03	Confidence interval of a mean	03_confidence_interval_mean.ipynb
04	Confidence interval of a proportion	04_confidence_interval_proportion.ipynb
05	Confidence interval of survival data	05_confidence_interval_survival_data.ipynb
06	Confidence interval of counted data	06_confidence_interval_counted_data.ipynb
07	Statistical significance	07_statistical_significance.ipynb
08	Statistical power and sample size	08_statistical_power_sample_size.ipynb
09	Normality tests and outliers	09_normality_tests_outliers.ipynb
10	Comparing proportions	10_comparing_proportions.ipynb
11	Comparing survival curves	11_comparing_survival_curves.ipynb
12	Comparing two unpaired means	12_comparing_two_unpaired_means.ipynb
13	Comparing paired data	13_comparing_paired_data.ipynb
14	Correlation	14_correlation.ipynb
15	Simple linear regression	15_simple_linear_regression.ipynb
16	Comparing models	16_comparing_models.ipynb
17	Nonlinear regression	17_nonlinear_regression.ipynb
18	Multiple regression	18_multiple_regression.ipynb
19	Logistic regression	19_logistic_regression.ipynb
20	ANOVA	20_anova.ipynb
21	Meta-analysis	21_meta_analysis.ipynb
App. A	Analysis of an ex vivo experiment	A_analysis_ex_vivo_experiment.ipynb

---

🧬 Datasets

The data/ directory contains all raw data files used across the analyses:

• animals.csv — Animal measurement data
• cytometry.csv — Flow cytometry experimental data
• data_final.csv — Compiled clinical and experimental cohort data
• ifng_data.csv — Interferon-gamma (IFN-γ) assay measurements
• smp2.csv & Pr_sentation_variables__tude_SMP_MOOC_R.pdf — "Santé Mentale en Prison (SMP)" study dataset and clinical variable documentation

---

💻 Installation & Running the Code

Option 1: Run Locally

Requires Python 3.12 installed on your system.

1. Clone the repository:

git clone https://github.com/sbwiecko/intuitive-biostatistics-python.git
cd intuitive-biostatistics-python

2. Create and activate a virtual environment:

python -m venv venv

# On Windows (PowerShell):
venv\Scripts\Activate.ps1

# On macOS/Linux:
source venv/bin/activate

3. Install required dependencies:

pip install -r requirements.txt

4. Launch Jupyter Notebook or JupyterLab:

jupyter notebook
# or: jupyter lab

Option 2: Run in the Cloud (No Installation Required)

Click the Launch Binder badge below to run the notebooks in an interactive Jupyter environment in your browser:

Option 3: Run with Anaconda (Book's Graphical Environment)

As featured in the book, Anaconda provides an all-in-one data science distribution including Python, conda, and pre-curated packages. It features Anaconda Navigator, a point-and-click desktop application (GUI) ideal for managing environments and launching Jupyter Notebook without using the command line.

1. Install Anaconda:

• Download the full "Distribution Installer" for your operating system from the official Anaconda download page.
• Run the installer using the default options (see the troubleshooting guide if needed).

2. Replicate the Book's Environment (intuitive_biostatistics_python):

• Via Anaconda Navigator (GUI):

  1. Open Anaconda Navigator and go to the Environments tab on the left.
  2. Click Import at the bottom of the environment list.
  3. Select Local drive, browse to and select the repository's environment.yml file, and click Import.
  4. Once downloaded and installed, activate intuitive_biostatistics_python by clicking the green arrow or selecting it in the environment list.

• Via Terminal (conda CLI alternative):

  conda env create -f environment.yml
  conda activate intuitive_biostatistics_python

3. Launch Jupyter Notebook:

• Return to the Home tab in Anaconda Navigator and verify that the dropdown shows "All applications on intuitive_biostatistics_python".
• Locate Jupyter Notebook and click Launch (this opens Jupyter in your default web browser).
• In the Jupyter file browser, navigate to the folder containing your companion notebooks and click any .ipynb file to open it (or select New > Python 3 (ipykernel) to start a fresh notebook).
• Run cells using Shift + Enter or via Run > Run Selected Cell.
• When finished, save your work, go to the Running tab in Jupyter to shut down running kernels, and safely exit Anaconda Navigator.

---

💬 Community, Q&A, and Errata

This repository serves as an open forum for readers:

• 🙋 Questions & Discussions:
  Use GitHub Discussions to ask conceptual questions about the book, discuss statistical interpretations, or share how you applied the code to your lab's data.
• 🐛 Errata & Code Bugs:
  Found a typo in a notebook, an outdated dependency, or an erratum? Please open an Issue specifying the chapter and notebook affected.
• 🤝 Pull Requests:
  Community contributions and typos fixes are welcome! Feel free to open a Pull Request for code corrections, comments clarity, or dependency compatibility updates.

---

📚 How to Access the Book

• 🏛️ Online Reading & Institutional Access (Oxford Academic):
  Available via persistent DOI: doi.org/10.1093/oso/9780197845028.001.0001. University and research institute members can access the full electronic chapters through their library subscriptions (Shibboleth/OpenAthens/IP).
• 🛒 Print & Commercial E-Book Editions (Hardcover, Paperback, E-Book):
  Order physical hardbound/paperback copies or individual digital editions directly through the Oxford University Press Catalog or your preferred academic bookseller.

🔖 Citation & Bibliographic Metadata

@book{wieckowskiIntuitiveBiostatisticsPython2026,
	author = {Wieckowski, Sébastien},
	title = {Intuitive biostatistics with python: a python companion guide for life sciences professionals},
	publisher = {Oxford University Press},
	year = {2026},
	doi = {10.1093/oso/9780197845028.001.0001},
	edition = {1},
	isbn = {978-0-19-784502-8 978-0-19-784588-2},
	url = {https://academic.oup.com/book/63332}
}

• Print ISBN: 9780197845028 (Hardcover / Paperback)
• Online ISBN: 9780197845882 (Oxford Academic / Electronic)
• DOI: 10.1093/oso/9780197845028.001.0001

---

📄 License & Copyright

• Code & Notebooks: All Python scripts and Jupyter Notebooks (*.ipynb) in this repository are released under the MIT License. You are free to use, modify, and distribute the code for academic, scientific, and commercial purposes.
• Book Content & Cover: The book cover (cover.png), explanatory prose, and pedagogical materials from Intuitive Biostatistics with Python are copyrighted by Oxford University Press. All rights reserved.
• Datasets: The sample datasets in data/ are provided for educational and reproducibility purposes alongside the text; any third-party clinical or study datasets remain subject to their respective original sources and terms of use.