{"product_id":"avoiding-data-pitfalls-isbn-9781119278160","title":"Avoiding Data Pitfalls","description":"\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 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 \u003ci\u003eonly\u003c\/i\u003e 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. \u003c\/p\u003e\u003cp\u003eWorkers 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 \u003ci\u003echasms\u003c\/i\u003e—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. \u003c\/p\u003e\u003cul\u003e \u003cli\u003eDelve into the \"data-reality gap\" that grows with our dependence on data\u003c\/li\u003e \u003cli\u003eLearn how the right tools can streamline the visualization process\u003c\/li\u003e \u003cli\u003eAvoid common mistakes in data analysis, visualization, and presentation\u003c\/li\u003e \u003cli\u003eCreate and present clear, accurate, effective data visualizations\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eTo 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 \u003ci\u003eAvoiding Data Pitfalls\u003c\/i\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  \u003cp\u003e\u003cb\u003eBEN JONES\u003c\/b\u003e 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 \u003ci\u003eCommunicating Data with Tableau\u003c\/i\u003e and \u003ci\u003e17 Key Traits of Data Literacy\u003c\/i\u003e, 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.   \u003c\/p\u003e\u003cp\u003e\"Data has rarely gotten more personal than this. Ben Jones's \u003ci\u003eAvoiding Data Pitfalls\u003c\/i\u003e isn't just a rehash of classics such as \u003ci\u003eHow to Lie With Statistics\u003c\/i\u003e; 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.\" \u003cb\u003e —Alberto Cairo,\u003c\/b\u003e author of \u003ci\u003eHow Charts Lie\u003c\/i\u003e \u003c\/p\u003e\u003cp\u003e\"Humans aren't perfect and neither is data. This book gives valuable advice on how to proceed with those truths in mind.\" \u003cb\u003e —Giorgia Lupi,\u003c\/b\u003e partner at Pentagram; co-author of \u003ci\u003eDear Data\u003c\/i\u003e \u003c\/p\u003e\u003cp\u003e\u003cb\u003eLEARN AND MASTER THE LANGUAGE OF DATA\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eData 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 disposal—resulting in common mistakes that could easily have been avoided with some expert advice. \u003c\/p\u003e\u003cp\u003e\u003ci\u003eAvoiding Data Pitfalls\u003c\/i\u003e 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.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47988782760165,"sku":"NP9781119278160","price":49.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119278160.jpg?v=1761781571","url":"https:\/\/k12savings.com\/es\/products\/avoiding-data-pitfalls-isbn-9781119278160","provider":"K12savings","version":"1.0","type":"link"}