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Grokking Statistics: A friendly guide for beginners
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"An excellent, playful introduction to foundational statistical ideas and calculations."
—Matthew Housley, Co-author of Fundamentals of Data Engineering
Grokking Statistics makes the core ideas of statistics intuitive, practical, and surprisingly entertaining. Using relatable examples, illustrations, and clear explanations, Thomas Nield shows you how to reason about uncertainty, draw useful conclusions from data, and recognize when the numbers themselves may be misleading. You’ll learn to move beyond deterministic thinking and develop the skeptical, questioning mindset that good statistical analysis requires.
Starting with the relationship between samples and populations, you’ll build a practical understanding of probability distributions, the normal distribution, the central limit theorem, confidence intervals, hypothesis testing, and linear and logistic regression. Along the way, you’ll apply these ideas to examples ranging from manufacturing tolerances and business decisions to tornado data and toddler meltdowns. Python handles the calculations so you can concentrate on what the results mean and when you should trust them.
Just as importantly, Grokking Statistics teaches you to question data itself. You’ll learn to look for biased samples, questionable assumptions, misleading metrics, overfitting, p-hacking, and other ways statistical reasoning can go wrong. These are essential practical skills. They matter whether you’re evaluating a research finding, analyzing business data, building a software product, or assessing the performance of a machine learning model.
Clear Python examples explain the concepts throughout the book. Thomas’s practical, sometimes whimsical approach keeps the focus on understanding rather than memorizing formulas. Reviewer Mark J. Miller of Route.com says, “With practical, entertaining examples, you’ll learn key principles and skills that will level up your BS detector!” Reviewer Thomas Briegel of Plaurag Edelmetalle GmbH calls it an “accessible and practical introduction to statistics that avoids intimidating math.”
True to the promise of “grokking,” this book brings your understanding of statistics to a point deep enough to use confidently—and to know how to question conclusions drawn from data. You’ll come away ready to make smarter decisions under uncertainty, evaluate claims critically, and put statistical thinking to work wherever data is involved.
What's inside
• Quantify real-world uncertainty
• Evaluate classification models using precision and recall metrics
• Avoid devastating logical fallacies, data drift, and p-hacking
About the reader
This book is for software developers, data analysts, and tech-adjacent professionals who want to build a practical, intuitive, and confident understanding of statistics. Code examples use Python.
About the author
Thomas Nield is an instructor at the University of Southern California and the founder of Nield Consulting Group and Yawman Flight. He has over a decade of experience in aviation, logistics, and data science, specializing in translating complex mathematical concepts into practical, industry-standard solutions.
"An excellent, playful introduction to foundational statistical ideas and calculations."
—Matthew Housley, Co-author of Fundamentals of Data Engineering
Grokking Statistics makes the core ideas of statistics intuitive, practical, and surprisingly entertaining. Using relatable examples, illustrations, and clear explanations, Thomas Nield shows you how to reason about uncertainty, draw useful conclusions from data, and recognize when the numbers themselves may be misleading. You’ll learn to move beyond deterministic thinking and develop the skeptical, questioning mindset that good statistical analysis requires.
Starting with the relationship between samples and populations, you’ll build a practical understanding of probability distributions, the normal distribution, the central limit theorem, confidence intervals, hypothesis testing, and linear and logistic regression. Along the way, you’ll apply these ideas to examples ranging from manufacturing tolerances and business decisions to tornado data and toddler meltdowns. Python handles the calculations so you can concentrate on what the results mean and when you should trust them.
Just as importantly, Grokking Statistics teaches you to question data itself. You’ll learn to look for biased samples, questionable assumptions, misleading metrics, overfitting, p-hacking, and other ways statistical reasoning can go wrong. These are essential practical skills. They matter whether you’re evaluating a research finding, analyzing business data, building a software product, or assessing the performance of a machine learning model.
Clear Python examples explain the concepts throughout the book. Thomas’s practical, sometimes whimsical approach keeps the focus on understanding rather than memorizing formulas. Reviewer Mark J. Miller of Route.com says, “With practical, entertaining examples, you’ll learn key principles and skills that will level up your BS detector!” Reviewer Thomas Briegel of Plaurag Edelmetalle GmbH calls it an “accessible and practical introduction to statistics that avoids intimidating math.”
True to the promise of “grokking,” this book brings your understanding of statistics to a point deep enough to use confidently—and to know how to question conclusions drawn from data. You’ll come away ready to make smarter decisions under uncertainty, evaluate claims critically, and put statistical thinking to work wherever data is involved.
What's inside
• Quantify real-world uncertainty
• Evaluate classification models using precision and recall metrics
• Avoid devastating logical fallacies, data drift, and p-hacking
About the reader
This book is for software developers, data analysts, and tech-adjacent professionals who want to build a practical, intuitive, and confident understanding of statistics. Code examples use Python.
About the author
Thomas Nield is an instructor at the University of Southern California and the founder of Nield Consulting Group and Yawman Flight. He has over a decade of experience in aviation, logistics, and data science, specializing in translating complex mathematical concepts into practical, industry-standard solutions.
570 pages, Kindle Edition
Published October 6, 2026
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Displaying 1 - 1 of 1 review
June 4, 2026
I had the pleasure of reading this through Manning’s excellent early access program, MEAP. Since I read an unpublished version of the book, please keep in mind that I have not read a single page of chapter 4, which I believe through context will be on the CLT and Law of Large Numbers.
I found this book to be a leisurely introduction or refresher on fundamental statistics. The final chapter is a surprisingly insightful and generalizable guide on how to navigate your career, which I found particularly helpful as I am very early in my career. If you enjoy reading about statistics, I would recommend, but do not expect a highly technical textbook. There are python examples for most of the concepts covered, for those interested in some practical applications.
I found this book to be a leisurely introduction or refresher on fundamental statistics. The final chapter is a surprisingly insightful and generalizable guide on how to navigate your career, which I found particularly helpful as I am very early in my career. If you enjoy reading about statistics, I would recommend, but do not expect a highly technical textbook. There are python examples for most of the concepts covered, for those interested in some practical applications.
Displaying 1 - 1 of 1 review