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Intuitive Statistics

Mastering basics, Bayes, and AI

Intuitive Statistics
This book is 90% completeLast updated on 2026-10-01

The hardest part of stats is understanding how it all fits together. If you want a book that takes you from the very basics all the way through to the most advanced concepts, this is for you!

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About

About

About the Book

An approachable, self-contained, and manageable course in statistics. Starting out with the fundamentals of statistics (e.g., how to compare averages and what a p-value is), by the end you will be able to fit advanced Bayesian models and even use neural networks to analyse images. Developed over ten years of teaching at universities in the UK, US, and Canada, this book comes with worked examples in R for you to use in your own work.

Topics covered in the chapters include:

  1. Fundamentals. What a probability is, what statistical distributions are, and how to plot out data.
  2. Test statistics. How to calculate a 't-test', and a simple trick to move from t-tests to regressions and ANOVA.
  3. Multiple models. How to compare between models, either using p-value significance tests or cutting-edge model selection methods that avoid 'p-hacking'.
  4. Generalised linear models. Use this family of models when you want to explore data that aren't continuous, like yes/no answers in surveys, or counts of things that can't contain halves (e.g., 1, 2, but never 1.5).
  5. Bayesian models. A powerful family of models that make it easy to deal with complicated data structures and present results in plain English to stakeholders.
  6. Spatial and temporal data. Account for repeated measurements across space and time by accounting for these kinds of bias.
  7. The history of machine learning. Confused about where ChatGPT and such models came from? Learn about their 'parents' - like 'shrdlu' from 1970!
  8. Unsupervised machine learning. Group similar kinds of data together and simplify complicated datasets.
  9. Supervised machine learning. Use tricks from regression trees, support vector machines, and lasso regression to quickly find answers in large datasets.
  10. Neural networks. Fit your own deep learning models and unlock the power of AI.

As well as PDF and ePub versions, you will have access to (1) a password-protected online version of the book for easy copy-pasting of code, (2) a link to all datasets used in the book, and (3) laptop recordings of the author explaining the topics.

Every chapter of the book is written; I am currently adding in last pieces and giving it one final proof-read. I have used these materials for >10 years, so I'm confident of them, but I'm giving a discount now (buy for only £5!) while I'm proof-reading. Buy now and save! You will have access to updates and the finished version if you buy now.

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Author

About the Author

William D. Pearse

I'm an Associate Professor at Imperial College London, where I lead a research group focusing on biodiversity, health, statistics, and computing. I have published >100 scientific papers on topics ranging from designing global conservation strategies, forecasting COVID dynamics, and understanding how flowers respond to climate change. I have led and contributed to numerous R packages (e.g., caper, SymbiotaR2, pez, ), which have in total been downloaded millions of times.

I have taught under- and post-graduate courses in biology, statistics, and computing for >10 years. My statistics courses are popular with students; my undergraduate students voted me the winner of the 2009 'Faculty Teaching Award' while I was a PhD student, my postgraduate students voted me 'Best Supervisor' in 2021, and I received a 'Department Teaching Award' in 2024.

When I'm not doing any of the above, I spend time with my family, a cute whippet named Coco, and two excitable gerbils named Marshmallow and Chestnut.

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