Avoiding data pitfalls : how to steer clear of common blunders when working with data and presenting analysis and visualizations /: how to steer clear of common blunders when working with data and presenting analysis and visualizations. (2017)
- Record Type:
- Book
- Title:
- Avoiding data pitfalls : how to steer clear of common blunders when working with data and presenting analysis and visualizations /: how to steer clear of common blunders when working with data and presenting analysis and visualizations. (2017)
- Main Title:
- Avoiding data pitfalls : how to steer clear of common blunders when working with data and presenting analysis and visualizations
- Further Information:
- Note: Ben Jones.
- Authors:
- Jones, Ben
- 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 HowPreface 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 … (more)
- Edition:
- 1st
- Publisher Details:
- Hoboken, New Jersey : John Wiley & Sons, Inc
- Publication Date:
- 2017
- Extent:
- 1 online resource
- Subjects:
- 658.4038
Information technology -- Management
Information visualization
Business -- Data processing - Languages:
- English
- ISBNs:
- 9781119278177
9781119278191 - Notes:
- Note: Description based on CIP data; resource not viewed.
- Access Rights:
- Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
- Access Usage:
- Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD.DS.472976
- Ingest File:
- 02_622.xml