Avoiding Data Pitfalls

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.

Autorentext

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 doand what not to dowhen working with data.

Klappentext
"Data has rarely gotten more personal than this. Ben Jones's Avoiding Data Pitfalls isn't just a rehash of classics such as How to Lie With Statistics; rather, it's a refreshing, honest, idiosyncratic, and deeply humane take on the hurdles we all face when gathering, analyzing, or presenting data, written from the point of view of a professional who's seen and erred a lot, and who's not afraid of acknowledging it." Alberto Cairo, author of How Charts Lie "Humans aren't perfect and neither is data. This book gives valuable advice on how to proceed with those truths in mind." Giorgia Lupi, partner at Pentagram; co-author of Dear Data LEARN AND MASTER THE LANGUAGE OF DATA Data pitfalls are all around us; anyone who has worked with data has fallen into them many times. Sometimes we fall into them without even noticing, only to find out much later. It is an all-too-common scenario: you've prepared an impeccable presentation, complete with beautiful charts and bullet-proof insights, only to be informed that the database you're working with is flawed. Most of us were not taught how to work with the modern tools and types of data at our disposalresulting in common mistakes that could easily have been avoided with some expert advice. Avoiding Data Pitfalls shows you how to spare yourself and your colleagues from embarrassing blunders and costly mistakes when working with data. This invaluable guide offers real-world examples of common errors and provides step-by-step guidance on successfully visualizing and presenting your data. You will learn to identify and avoid the seven types of data pitfalls, such as cluttered design and ineffective use of color, and create accurate and effective presentations.

Zusammenfassung
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 thembut 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 therein 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 pitfallssome 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.

Inhalt
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 E…