{"product_id":"avoiding-data-pitfalls-9781119278160","title":"Avoiding Data Pitfalls","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eAvoid data blunders and create truly useful visualizations\u003c\/b\u003e \u003cp\u003e\u003ci\u003eAvoiding Data Pitfalls\u003c\/i\u003e 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 \u003ci\u003eonly\u003c\/i\u003e 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 i\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003ePreface ix\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 The Seven Types of Data Pitfalls 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSeven Types of Data Pitfalls 3\u003c\/p\u003e \u003cp\u003ePitfall 1: Epistemic Errors: How We Think About Data 3\u003c\/p\u003e \u003cp\u003ePitfall 2: Technical Traps: How We Process Data 4\u003c\/p\u003e \u003cp\u003ePitfall 3: Mathematical Miscues: How We Calculate Data 4\u003c\/p\u003e \u003cp\u003ePitfall 4: Statistical Slipups: How We Compare Data 5\u003c\/p\u003e \u003cp\u003ePitfall 5: Analytical Aberrations: How We Analyze Data 5\u003c\/p\u003e \u003cp\u003ePitfall 6: Graphical Gaffes: How We Visualize Data 6\u003c\/p\u003e \u003cp\u003ePitfall 7: Design Dangers: How We Dress up Data 6\u003c\/p\u003e \u003cp\u003eAvoiding the Seven Pitfalls 7\u003c\/p\u003e \u003cp\u003e“I’ve Fallen and I Can’t Get Up” 8\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 Pitfall 1: Epistemic Errors 11\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eHow We Think About Data 11\u003c\/p\u003e \u003cp\u003ePitfall 1A: The Data-Reality Gap 12\u003c\/p\u003e \u003cp\u003ePitfall 1B: All Too Human Data 24\u003c\/p\u003e \u003cp\u003ePitfall 1C: Inconsistent Ratings 32\u003c\/p\u003e \u003cp\u003ePitfall 1D: The Black Swan Pitfall 39\u003c\/p\u003e \u003cp\u003ePitfall 1E: Falsifiability and the God Pitfall 43\u003c\/p\u003e \u003cp\u003eAvoiding the Swan Pitfall and the God Pitfall 44\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 Pitfall 2: Technical Trespasses 47\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eHow We Process Data 47\u003c\/p\u003e \u003cp\u003ePitfall 2A: The Dirty Data Pitfall 48\u003c\/p\u003e \u003cp\u003ePitfall 2B: Bad Blends and Joins 67\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 Pitfall 3: Mathematical Miscues 74\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eHow We Calculate Data 74\u003c\/p\u003e \u003cp\u003ePitfall 3A: Aggravating Aggregations 75\u003c\/p\u003e \u003cp\u003ePitfall 3B: Missing Values 83\u003c\/p\u003e \u003cp\u003ePitfall 3C: Tripping on Totals 88\u003c\/p\u003e \u003cp\u003ePitfall 3D: Preposterous Percents 93\u003c\/p\u003e \u003cp\u003ePitfall 3E: Unmatching Units 102\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 Pitfall 4: Statistical Slipups 107\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eHow We Compare Data 107\u003c\/p\u003e \u003cp\u003ePitfall 4A: Descriptive Debacles 109\u003c\/p\u003e \u003cp\u003ePitfall 4B: Inferential Infernos 131\u003c\/p\u003e \u003cp\u003ePitfall 4C: Slippery Sampling 136\u003c\/p\u003e \u003cp\u003ePitfall 4D: Insensitivity to Sample Size 142\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 Pitfall 5: Analytical Aberrations 148\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eHow We Analyze Data 148\u003c\/p\u003e \u003cp\u003ePitfall 5A: The Intuition\/Analysis False Dichotomy 149\u003c\/p\u003e \u003cp\u003ePitfall 5B: Exuberant Extrapolations 157\u003c\/p\u003e \u003cp\u003ePitfall 5C: Ill-Advised Interpolations 163\u003c\/p\u003e \u003cp\u003ePitfall 5D: Funky Forecasts 166\u003c\/p\u003e \u003cp\u003ePitfall 5E: Moronic Measures 168\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 Pitfall 6: Graphical Gaffes 173\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eHow We Visualize Data 173\u003c\/p\u003e \u003cp\u003ePitfall 6A: Challenging Charts 175\u003c\/p\u003e \u003cp\u003ePitfall 6B: Data Dogmatism 202\u003c\/p\u003e \u003cp\u003ePitfall 6C: The Optimize\/Satisfice False Dichotomy 207\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 Pitfall 7: Design Dangers 212\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eHow We Dress up Data 212\u003c\/p\u003e \u003cp\u003ePitfall 7A: Confusing Colors 214\u003c\/p\u003e \u003cp\u003ePitfall 7B: Omitted Opportunities 222\u003c\/p\u003e \u003cp\u003ePitfall 7C: Usability Uh-Ohs 227\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 Conclusion 237\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAvoiding Data Pitfalls Checklist 241\u003c\/p\u003e \u003cp\u003eThe Pitfall of the Unheard Voice 243\u003c\/p\u003e \u003cp\u003eIndex 247 \u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49407026135383,"sku":"9781119278160","price":30.39,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781119278160.jpg?v=1730497918","url":"https:\/\/bookcurl.com\/products\/avoiding-data-pitfalls-9781119278160","provider":"Book Curl","version":"1.0","type":"link"}