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.
Tabla de materias
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
Sobre el autor
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.
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