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Storytelling with Data
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2025
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Pages
376
Published
2019
Data Analytics
✨ New
The Art of Statistics
How to Learn from Data
Master the statistical reasoning that separates meaningful conclusions from noise, using real problems and everyday examples.
David Spiegelhalter, one of Britain's most respected statisticians, shows you how to think with data rather than just calculate with it. From summarizing messy datasets to understanding uncertainty and risk, this book builds the analytical habits that matter in practice. Whether you are entering the analytics field or sharpening an existing career, it gives you the conceptual tools to ask better questions and draw honest conclusions from evidence.
About this book
Statistics is not primarily about formulas. It is about judgment: knowing what a number actually tells you, when to trust a result, and when to be suspicious. David Spiegelhalter wrote this book to close the gap between the statistics people are taught and the statistical thinking they actually need.
Every chapter starts with a real problem — a medical study, a crime figure, a polling result — and uses it to build a concept from the ground up. You learn why averages mislead, what a p-value really tests, how visualizations can clarify or distort, and why the same data can support contradictory headlines. The examples are current, the explanations are plain, and the reasoning applies directly to the kind of analysis you encounter at work and in the news.
The book covers the full data-analysis pipeline: gathering data, exploring and summarizing it, building and checking models, quantifying uncertainty, and communicating results honestly. Each stage connects to a body of statistical practice that Spiegelhalter has spent decades applying and teaching.
- Understand what summary statistics do and do not reveal about a distribution
- Interpret confidence intervals and p-values without the standard misconceptions
- Recognize the sources of bias that corrupt observational studies
- Evaluate claims about risk and probability in media, medicine, and policy
- Apply Bayesian reasoning to update beliefs as evidence arrives
This is not a textbook. There are no problem sets and no derivations. It is a sustained argument for a certain way of thinking, written by someone who has advised governments, courts, and public health bodies on exactly the issues the book covers. By the end, you will read a statistical claim differently than you did before — more skeptically, more precisely, and with a clearer sense of what it would take to actually believe it.
🎯 What you'll learn
- Distinguish between what data shows and what it proves, and explain that difference to others
- Interpret p-values, confidence intervals, and significance correctly without the common distortions
- Identify the types of bias that corrupt surveys, experiments, and observational studies
- Evaluate risk and probability claims using tools like expected frequency and Bayesian updating
- Assess whether a statistical model fits reality or merely fits the numbers it was trained on
- Recognize when a visualization is clarifying and when it is misleading
- Apply the PPDAC cycle — Problem, Plan, Data, Analysis, Conclusion — to real analytical questions
👤 Who is this book for?
- Analysts and data professionals who want to strengthen the conceptual foundations behind the tools they already use
- Students entering a data, economics, or social science program who need statistical intuition before the formulas arrive
- Developers and engineers who work with data but received little formal statistics training
- Managers and decision-makers who must evaluate evidence-based reports and research findings
- Journalists, policy professionals, or researchers who interpret statistical claims as part of their daily work
- Curious general readers who want to think more clearly about numbers in public life
Table of contents
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01
Getting Things in Proportion
Introduces the PPDAC problem-solving cycle and shows how framing a question precisely is the first and most consequential step in any statistical investigation.
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02
Summarizing and Communicating Numbers
Explores measures of location and spread, explaining what they reveal and what they hide, and how to choose the right summary for a given situation.
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03
Why Visualizations Work and When They Lie
Examines how well-designed charts build understanding and how common design choices distort the underlying data, with examples drawn from news and science.
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04
Regression and Relationships
Covers correlation and regression, including the famous regression-to-the-mean phenomenon that causes so many misinterpretations of repeated measurements.
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05
Probability and Its Limits
Builds a working vocabulary for probability, distinguishing between long-run frequency and degrees of belief, and connects both to everyday decisions under uncertainty.
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06
Bayesian Thinking
Introduces Bayes' theorem through medical testing and legal evidence, showing how prior knowledge and new data combine to produce updated beliefs.
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07
Uncertainty, Confidence, and Significance
Unpacks what confidence intervals and p-values actually measure, corrects the most persistent misreadings, and explains what statistical significance cannot tell you.
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08
Cause and Effect
Distinguishes association from causation, surveys the hierarchy of evidence from randomized trials to observational studies, and identifies the biases that corrupt each design.
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09
Learning from Experience
Draws together the book's core themes to show how statistical reasoning accumulates across studies, errors get corrected, and knowledge in science and policy actually advances.
Frequently asked questions
Do I need a mathematics background to read this book?
No. The book requires no calculus, algebra, or prior statistics coursework. Spiegelhalter explains every concept in plain language, using concrete examples rather than derivations.
Is this a textbook with exercises and problem sets?
No. It is a narrative book written for a general and professional audience. There are no exercises, exams, or companion datasets, though the real-world examples throughout are detailed enough to apply directly.
Is this book relevant if I already use statistical software like R or Python?
Yes. The book addresses the reasoning behind the numbers rather than the mechanics of producing them. Many working analysts find it strengthens the judgment they apply to results their tools already generate.
How current is the content given the 2019 publication date?
The core statistical concepts are timeless, and the examples were contemporary at publication. A small number of case studies reference events from the mid-2010s, but none of the conceptual material is dated.
Who is the author and why does his perspective matter?
David Spiegelhalter is a statistician at the University of Cambridge and a former President of the Royal Statistical Society. He has advised public health bodies, courts, and government on statistical evidence.
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