Recommended Literature#
These are some of my favorite books on statistics, data analysis, and related topics. You might be faster starting with a web search or a video tutorial on the topic, or chatting with AI. But at some point, a textbook can be a great resource to dive deeper into a topic while providing a structured overview.
Most of these textbooks are on a scale from relatively to outrageously expensive, so try checking them out at a library first. If you need to buy a copy for highlighting and scribbling in the margins, consider buying used or from a local bookstore whenever possible! For the lazy ones, I list affiliate links to Amazon for every book below, but the recommendations are all independent.
Favorites, Introductory#
These are my go-to recommendations for everybody asking for a good starting point or a refresher in statistics. These are all very approachable (even fun) with plenty of examples and tutorials. They may not cover some more advanced topics, but they do go into detail, and offer plenty of eye-opening moments after actually understanding the problem.
Discovering Statistics by Andy Field#
This is the first textbook I actually liked. It’s funny, practical, engaging, and covers a lot of ground. The book is available for both R and SPSS users with very extensive tutorials, you will learn the software as well as the statistics. Using R: Amazon link and Using SPSS: Amazon link.
Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences by Jacob Cohen#
This is a classic textbook on regression analysis. If you want to master only one statistical method, this is the one to choose. The most common struggle among beginners is choosing the correct statistical test for each problem. Regression is always the correct answer. This book is a bit more technical but very well-written and gives you the insight you didn’t find in other textbooks. Check the Amazon link.
Computer Age Statistical Inference by Bradley Efron and Trevor Hastie#
This is a modern take on statistics. For those who still learn to calculate ANOVAs by hand, learning to program them might be better. This book focuses on the practical aspects of data analysis. It covers a lot of ground, from the basics to more advanced topics, and is a great resource for understanding the statistical methods used in machine learning. Check the Amazon link.
How to Design and Report Experiments by Andy Field and Graham Hole#
This book is more about experimental design than statistics, but it’s a great resource for understanding the process of research. It’s a great companion to the other textbooks, as it helps you understand how to set up your data collection to answer the questions you care about. Check the Amazon link.
For Fun, Popular Science#
For those of you who actually read for fun, here are some great books on statistics and data analysis that are both entertaining and informative.
Freakonomics by Steven Levitt and Stephen Dubner#
Still looking for the purpose of statistics? This book is a fascinating exploration of statistics applied to real-world problems in economics. Using data-driven insights, it uncovers surprising connections between incentives, behavior, and societal trends. Engaging and thought-provoking, it challenges conventional wisdom and offers a fresh perspective on everyday decisions and hidden forces shaping our world. Check the Amazon link.
Bad Science by Ben Goldacre#
More reasons to learn statistics, this book is a sharp, witty takedown of pseudoscience and misleading claims in medicine, media, and marketing. With clear explanations and real-world examples, Bad Science exposes how data is misused and why scientific literacy matters. A must-read for anyone wanting to think critically about health and science. Check the Amazon link.
Calculated Risks by Gerd Gigerenzer#
This book explores how we misunderstand probabilities and risk in everyday life, from medical diagnoses to financial decisions. Using clear explanations and real-world examples, Gigerenzer reveals how statistical thinking can help us make better choices. A must-read for anyone looking to improve their decision-making and risk literacy. Check the Amazon link.
The Joy of x and Infinite Powers by Steven Strogatz#
These two books are a great introduction to the beauty of mathematics. They cover a range of topics in a simple and engaging way. If you have ever had any regrets about ditching math after school, these are a great way to encourage curiosity about the world of mathematics. Check the Amazon link.
The Book of Why by Judea Pearl#
Tired of hearing that correlation does not imply causality? This book explores the science of causality, challenging traditional statistical approaches. It explains how causal reasoning helps us move beyond correlation to understand cause and effect. With accessible insights and real-world examples, this book is essential for anyone interested in data science, AI, and scientific discovery. Check the Amazon link.
Baby University Series by Chris Ferrie#
This series of board books is a great introduction to complex scientific topics both for young children and the adult reading aloud. The books cover a range of topics, from quantum physics to general relativity, in a simple and engaging way. They are a great way to introduce children to scientific concepts and encourage curiosity about the world. They will also challenge you to be honest with yourself about what you actually understand. Check the Amazon link.
Advanced stuff#
These are more advanced textbooks that cover topics not usually found in other introductory textbooks or courses. They are great resources for those looking to deepen their understanding of specific methods or explore new areas of research.
Introduction to Mediation, Moderation, and Conditional Process Analysis by Andrew Hayes#
Applied Multilevel Analysis by Jos Twisk#
Data Analysis Using Regression and Multilevel/Hierarchical Models by Andrew Gelman and Jennifer Hill#
Statistical Rethinking by Richard McElreath#
Introduction to Robust Estimation and Hypothesis Testing by Rand Wilcox#
Theory of Spatial Statistics by Marie-Colette van Lieshout#
Time Series Analysis by George Box, Gwilym Jenkins, Gregory Reinsel, and Greta Ljung#
Machine Learning#
Because AI is just statistics with serious computing power, here are some great textbooks on (relatively) modern techniques in machine learning.
Deep learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville#
Mathematics for Machine Learning by Marc Peter Deisenroth, A Aldo Faisal, and Cheng Soon Ong#
Pattern Recognition and Machine Learning by Christopher Bishop#
