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Avoiding Data Pitfalls How to Steer Clear of Common Blunders When Working with Data and Presenting Analysis and Visualizations

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  • Edition: 1st
  • Format: Paperback
  • Copyright: 2019-11-19
  • Publisher: Wiley
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Avoid data blunders and create truly useful visualizations

Avoiding Data Pitfalls is a reputation-saving handbook for those who work with data, designed to help you avoid the all-too-common blunders that occur in data analysis, visualization, and presentation. Plenty of data tools exist, along with plenty of books that tell you how to use them—but unless you truly understand how to work with data, each of these tools can ultimately mislead and cause costly mistakes. This book walks you step by step through the full data visualization process, from calculation and analysis through accurate, useful presentation. Common blunders are explored in depth to show you how they arise, how they have become so common, and how you can avoid them from the outset. Then and only then can you take advantage of the wealth of tools that are out there—in the hands of someone who knows what they're doing, the right tools can cut down on the time, labor, and myriad decisions that go into each and every data presentation.

Workers in almost every industry are now commonly expected to effectively analyze and present data, even with little or no formal training. There are many pitfalls—some might say chasms—in the process, and no one wants to be the source of a data error that costs money or even lives. This book provides a full walk-through of the process to help you ensure a truly useful result.

  • Delve into the "data-reality gap" that grows with our dependence on data
  • Learn how the right tools can streamline the visualization process
  • Avoid common mistakes in data analysis, visualization, and presentation
  • Create and present clear, accurate, effective data visualizations

To err is human, but in today's data-driven world, the stakes can be high and the mistakes costly. Don't rely on "catching" mistakes, avoid them from the outset with the expert instruction in Avoiding Data Pitfalls.

Author Biography

BEN JONES is the Founder and CEO of Data Literacy, LLC, a company that's on a mission to help people speak the language of data. He's the author of Communicating Data with Tableau and 17 Key Traits of Data Literacy, and he also teaches data visualization at the University of Washington's Continuum College. With over 20 years of experience working as a mechanical engineer, a continuous improvement project leader and mentor, and a business intelligence marketer, Ben has learned a great deal about what to do—and what not to do—when working with data.

Table of Contents

Preface ix

Chapter 1 The Seven Types of Data Pitfalls 1

Seven Types of Data Pitfalls 3

Pitfall 1: Epistemic Errors: How We Think About Data 3

Pitfall 2: Technical Traps: How We Process Data 4

Pitfall 3: Mathematical Miscues: How We Calculate Data 4

Pitfall 4: Statistical Slipups: How We Compare Data 5

Pitfall 5: Analytical Aberrations: How We Analyze Data 5

Pitfall 6: Graphical Gaffes: How We Visualize Data 6

Pitfall 7: Design Dangers: How We Dress up Data 6

Avoiding the Seven Pitfalls 7

“I’ve Fallen and I Can’t Get Up” 8

Chapter 2 Pitfall 1: Epistemic Errors 11

How We Think About Data 11

Pitfall 1A: The Data-Reality Gap 12

Pitfall 1B: All Too Human Data 24

Pitfall 1C: Inconsistent Ratings 32

Pitfall 1D: The Black Swan Pitfall 39

Pitfall 1E: Falsifiability and the God Pitfall 43

Avoiding the Swan Pitfall and the God Pitfall 44

Chapter 3 Pitfall 2: Technical Trespasses 47

How We Process Data 47

Pitfall 2A: The Dirty Data Pitfall 48

Pitfall 2B: Bad Blends and Joins 67

Chapter 4 Pitfall 3: Mathematical Miscues 74

How We Calculate Data 74

Pitfall 3A: Aggravating Aggregations 75

Pitfall 3B: Missing Values 83

Pitfall 3C: Tripping on Totals 88

Pitfall 3D: Preposterous Percents 93

Pitfall 3E: Unmatching Units 102

Chapter 5 Pitfall 4: Statistical Slipups 107

How We Compare Data 107

Pitfall 4A: Descriptive Debacles 109

Pitfall 4B: Inferential Infernos 131

Pitfall 4C: Slippery Sampling 136

Pitfall 4D: Insensitivity to Sample Size 142

Chapter 6 Pitfall 5: Analytical Aberrations 148

How We Analyze Data 148

Pitfall 5A: The Intuition/Analysis False Dichotomy 149

Pitfall 5B: Exuberant Extrapolations 157

Pitfall 5C: Ill-Advised Interpolations 163

Pitfall 5D: Funky Forecasts 166

Pitfall 5E: Moronic Measures 168

Chapter 7 Pitfall 6: Graphical Gaffes 173

How We Visualize Data 173

Pitfall 6A: Challenging Charts 175

Pitfall 6B: Data Dogmatism 202

Pitfall 6C: The Optimize/Satisfice False Dichotomy 207

Chapter 8 Pitfall 7: Design Dangers 212

How We Dress up Data 212

Pitfall 7A: Confusing Colors 214

Pitfall 7B: Omitted Opportunities 222

Pitfall 7C: Usability Uh-Ohs 227

Chapter 9 Conclusion 237

Avoiding Data Pitfalls Checklist 241

The Pitfall of the Unheard Voice 243

Index 247 

Supplemental Materials

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The New copy of this book will include any supplemental materials advertised. Please check the title of the book to determine if it should include any access cards, study guides, lab manuals, CDs, etc.

The Used, Rental and eBook copies of this book are not guaranteed to include any supplemental materials. Typically, only the book itself is included. This is true even if the title states it includes any access cards, study guides, lab manuals, CDs, etc.

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