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Naked Statistics
Stripping the Dread from the Data — A Plain-English Introduction to Statistics
Understand the statistical ideas that shape every headline, business decision, and scientific claim you encounter.
Naked Statistics strips away the formulas and jargon that make most people tune out, replacing them with plain-English explanations and real-world stories that show why statistics actually matters. Charles Wheelan walks you through probability, regression, inference, and more — not as abstract math, but as practical tools for making sense of a noisy world. By the end, you will read data-driven claims with genuine confidence and appropriate skepticism.
About this book
Statistics is everywhere: in the polling numbers that predict elections, the drug trials that approve medications, the A/B tests that redesign websites, and the economic data that drives policy. Most people nod along and trust the number. After reading this book, you will know whether that trust is warranted.
Charles Wheelan built his reputation explaining economics to general audiences, and he brings the same clear, story-first approach to statistics. Naked Statistics is not a textbook. There are no problem sets, no Greek-letter derivations to grind through. Instead, Wheelan uses well-chosen real cases — sports analytics, public health campaigns, financial fraud, census methodology — to show how statistical thinking actually works and where it routinely goes wrong.
The book covers the core ideas that matter most: what an average can hide, how probability misleads intuition, why correlation is not causation, what a p-value is really telling you, and how regression models are built and abused. Each concept is introduced through a story, explained in plain terms, and then stress-tested against examples where smart people got it wrong.
- Understand mean, median, standard deviation, and why choosing the wrong one distorts the story
- Grasp the central limit theorem — the single most powerful idea in applied statistics — without a single integral
- See how polling works, what margin of error actually means, and how sample bias poisons results
- Learn what a regression coefficient does and does not prove
- Recognize the most common ways statistical results are cherry-picked, framed, or simply misunderstood
This is not a book that turns you into a statistician. It is a book that makes you a more rigorous consumer of quantitative claims — the kind of reader who asks the right questions before accepting a headline at face value. That skill is useful in almost every professional field and in everyday life.
Readers with no math background beyond high-school algebra will follow every argument. Analysts and data professionals who already know the mechanics will find the conceptual framing valuable for explaining their work to non-technical colleagues.
🎯 What you'll learn
- Distinguish between mean, median, and mode and choose the right measure for a given dataset
- Explain probability and expected value without falling into the common intuition traps
- Interpret a p-value and understand what statistical significance does and does not guarantee
- Trace how the central limit theorem makes large-scale inference possible
- Read a regression output and identify what the coefficients and confidence intervals actually claim
- Spot the most common forms of selection bias, survivorship bias, and data dredging in published results
- Evaluate polling methodology and understand why sample design matters more than sample size
👤 Who is this book for?
- Professionals in business, policy, or journalism who encounter data regularly but lack formal statistical training
- Students taking their first statistics course who want intuition before formulas
- Data analysts who want a resource they can recommend to non-technical stakeholders
- Curious readers who want to evaluate news stories, polls, and scientific claims more critically
- Managers making decisions based on reports they only partially understand
Table of contents
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01
What Is the Point?
Wheelan establishes why statistical literacy matters by walking through cases where ignoring or misreading data led to costly real-world mistakes. You finish the chapter with a clear sense of what the book will and will not teach you.
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02
Descriptive Statistics
You learn what averages, medians, and standard deviations measure and, crucially, what they conceal. The chapter uses income distribution data to show how the choice of summary statistic shapes the story being told.
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03
Deceptive Description
This chapter catalogs the most common ways descriptive statistics mislead — from cherry-picked metrics to index construction that encodes assumptions. You develop a checklist of questions to ask whenever a number is presented as a summary.
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04
Correlation
Wheelan explains what a correlation coefficient measures and demonstrates through multiple examples why two variables moving together tells you nothing about which one causes the other. You also see how correlation is used legitimately as a first diagnostic step.
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05
Basic Probability
You build an intuitive grasp of probability by working through the birthday problem, the Monty Hall puzzle, and expected-value calculations. The chapter shows why human intuition about rare events and compound probabilities is systematically unreliable.
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06
The Monty Hall Problem and Other Problems with Probability
Extending the previous chapter, Wheelan examines conditional probability, Bayes' theorem, and the base-rate fallacy through medical testing and legal cases. You leave understanding why false positives are far more common than most people assume.
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07
The Importance of Data
This chapter covers how data is collected, why sample design determines result quality, and how selection bias and nonresponse bias corrupt studies before a single calculation is run. You learn to evaluate a study's methodology before trusting its conclusions.
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08
The Central Limit Theorem
Wheelan explains why large random samples produce normally distributed means regardless of the original data's distribution, and why this single fact underpins most of inferential statistics. You see this principle applied to quality control, polling, and clinical trials.
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09
Inference and Statistical Significance
You learn what a null hypothesis is, how a p-value is calculated conceptually, and what 'statistically significant' actually means — including all the ways researchers misinterpret or misuse the threshold. The chapter includes well-documented cases of p-hacking and publication bias.
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10
Regression Analysis
The final chapter introduces linear and logistic regression as tools for isolating the effect of one variable while controlling for others. Wheelan shows real policy and business examples where regression analysis produced insights that simpler methods missed.
Frequently asked questions
Do I need any math background to read this book?
High-school algebra is more than enough. The book deliberately avoids calculus and formal notation. Wheelan explains every concept through stories and examples rather than equations.
Is this a textbook with exercises and problem sets?
No. It is a narrative non-fiction book written for a general audience. There are no problem sets, homework questions, or answer keys.
Will this book be useful if I already work with data professionally?
It depends on what you need. If you want to sharpen your own intuition about statistical communication and bias, the book offers genuine value. If you are looking for technical depth — modeling techniques, software, or advanced inference — this book is not the right level.
How current is the material for a 2014 publication?
The core statistical concepts covered are foundational and do not go out of date. Some topical examples reference events from that era, but every underlying idea remains fully applicable today.
Does the book cover any software or coding tools?
No. This book is entirely conceptual. It does not reference R, Python, Excel, or any statistical software. It is about statistical thinking, not statistical computing.
Who is this book not right for?
Readers who already have a solid statistics foundation and want to advance their technical skills will find the level too introductory. Graduate students in quantitative fields or working data scientists looking for methodology depth should look elsewhere.
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