No Bullshit Guide to Statistics
===============================

The book
--------
The **No Bullshit Guide to Statistics** is now available to purchase via Gumroad: 
[gum.co/noBSstats](https://gum.co/noBSstats).
This book introduces statistics in a rigorous, yet accessible manner.
It is the result of seven years of research, writing, and editing by Ivan Savov and collaborators.
Read the [announcement blog post](https://minireference.com/blog/noBSstats-prerelease/)
for more details about the book contents and the eBook prerelease in October 2025.
If you want to learn stats, go and get it now.



Computational notebooks
-----------------------
- [`notebooks/`](./notebooks/README.md): list of notebooks that accompany the book.
- [`tutorials/`](./tutorials/appendix.md): tutorials that introduce technical prerequisites like
  [Python coding](./tutorials/python_tutorial.ipynb),
  data management with [Pandas](https://pandas.pydata.org/),
  and data visualization with [Seaborn](https://seaborn.pydata.org/).

Use this binder button to run the notebooks interactively: 
[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/minireference/noBSstats/main?labpath=notebooks).




Good news; bad news
-------------------
**The good news is that I'm going to teach you everything I know about statistics.**
This means I'll show you all the formulas and computations
required to understand statistics deeply. We'll learn multiple alternative approaches
for doing statistical calculations, including visualizations, math models, and Python code.
By the end of this book, you'll have developed practical skills for data management,
analytical stills for doing probability calculations, and gained valuable
experience with the various procedures in the statistics toolbox.


**The bad news is that I'm going to teach you everything I know about statistics.**
This means there will be a lot of equations,
and you'll need to concentrate to understand fancy math expression like
summations $\Sigma_{i=1}^n x_i$ and integrals $\int f_X(x) dx$.
You have to trust me that the math complexity is necessary complexity,
because knowing the math details will allow you to understand statistics better.

You'll also have to become comfortable with code examples that illustrate statistical procedures
expressed as Python commands. These code examples will keep us honest:
if the answers we obtain using math formulas are the same as the answers we obtain
by running the Python code, then we can be sure we're doing things right.
This is why these notebooks exist—so you can try things for yourself.



Links
-----
Check on the links below to learn more about the book:

- Concept maps from the books: [statistics\_concepts.pdf](https://minireference.com/static/conceptmaps/statistics_concepts.pdf)
- Book preview PDFs that contain the introductions from each chapter:
  - [noBSstats_part1_preview.pdf](https://minireference.com/static/excerpts/noBSstats_part1_preview.pdf) [223pp, 9MB]
  - [noBSstats_part2_preview.pdf](https://minireference.com/static/excerpts/noBSstats_part2_preview.pdf) [191pp, 16MB]
- [Detailed book outline](https://docs.google.com/document/d/1fwep23-95U-w1QMPU31nOvUnUXE2X3s_Dbk5JuLlKAY/edit#)
  (continuously updated; open for comments)
- Subscribe to the [mailing list](https://confirmsubscription.com/h/t/A17516BF2FCB41B2)
  for future updates about the book.
- This GitHub repo for this site: [github.com/minireference/noBSstats](https://github.com/minireference/noBSstats/)
- The Python module [`ministats`](https://github.com/minireference/ministats) with statistics helper functions.
- A [playlist with video tutorials](https://www.youtube.com/playlist?list=PLGmu4KtWiH6-WQrTReNIIQhLxU8cirdxA)
  for Chapter 3 (the most complicated stuff).