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Ebook About Getting numbers is easy; getting numbers you can trust is hard. This practical guide by experimentation leaders at Google, LinkedIn, and Microsoft will teach you how to accelerate innovation using trustworthy online controlled experiments, or A/B tests."A/B testing is the gold standard of creating verifiable and repeatable experiments, and this book is its definitive text" -- Steve Blank, father of modern entrepreneurship, author of The Startup Owner's Manual and The Four Steps to the Epiphany"This book is a great resource for executives, leaders, researchers or engineers looking to use online controlled experiments" -- Harry Shum, Executive Vice President, Microsoft Artificial Intelligence and Research Group"A great book that is both rigorous and accessible. Readers will learn how to bring trustworthy controlled experiments, which have revolutionized internet product development, to their organizations" -- Adam D'Angelo, Co-founder and CEO of Quora and prior CTO of Facebook "Kohavi, Tang and Xu have a wealth of experience and excellent advice to convey, so the book has lots of practical real world examples and lessons learned over many years of the application of these techniques at scale." -- Jeff Dean, Google Senior Fellow, and SVP, Google Research"The secret sauce for a successful online business is experimentation. But it is a secret no longer. Here three masters of the art describe the ABCs of A/B testing so that you too can continuously improve your online services." -- Hal Varian, Chief Economist, Google, and author of Intermediate Microeconomics: A Modern Approach"This is the new bible of how to get from data to decisions in the digital age." -- Scott Cook, Intuit Co-founder & Chairman of the executive committeeBased on practical experiences at companies that each run more than 20,000 controlled experiments a year, the authors share examples, pitfalls, and advice for students and industry professionals getting started with experiments, plus deeper dives into advanced topics for practitioners who want to improve the way they make data-driven decisions. Learn how to Use the scientific method to evaluate hypotheses using controlled experiments. Define key metrics and ideally an Overall Evaluation Criterion. Test for trustworthiness of the results and alert experimentersto violated assumptions. Build a scalable platform that lowers the marginal cost of experiments close to zero. Avoid pitfalls like carryover effects and Twyman's law * Understand how statistical issues play out in practice.Book Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing Review :
First, I very much still believe what I wrote as a blurb for this book:"Internet companies have taken experimentation to an unprecedented scale, pace, and sophistication. These authors have played key roles in these developments and readers are fortunate to be able to learn from their combined experiences."These are exactly the right authors for a book with this topic and intended audience. I'm a co-organizer of the Conference on Digital Experimentation (CODE) at MIT, and we've had Ronny Kohavi and Ya Xu as invited speakers — in fact I think we've had Ronny as an invited speaker every year! This expertise and ability to communicate comes through in this book.I've also now had a chance to teach with this book at MIT, where it has been immensely helpful in teaching Marketing Analytics for MBA, Masters of Business Analytics, and other students. (I think this may have been the first use of the final text in the classroom, as the authors and publisher helpfully provided us with early access copies.)"Trustworthy Online Controlled Experiments" is rigorous and detailed enough to make it appropriate for a course, but also deeply connected with practical considerations, which is especially important for professional degree programs. The examples are tilted towards Internet companies, but much of the material is useful more broadly, especially as more and more experiments will be deployed through similar software stacks.For other instructors, here's what we've used from the book, in the order I used them:Session titles | Book chaptersMetrics, customer lifetime value & ROI | Ch 6Randomized experiments Part I & II | Ch 1-2, Ch 17Advertising and marketing experiments | Ch 18Product experimentation | Ch 3-4, 7 & 23Targeting marketing interventions | Ch 20Network data and multiple units | Ch 22This book largely replaces my use of "Field Experiments" by Gerber & Green, though I still point more technical students to that as a resource. A quick summary:A/B testing can be a powerful tool for IT companies to test out new ideas and provide data-driven evidence for innovation. However, without a correct mindset or appropriate design, A/B testing can go wrong. The corresponding results will not be trustworthy, and conclusions can be misleading and even damaging. The authors encourage readers to evaluate the trustworthiness of experiment results and share important practical lessons. This book will help a wide range of readers (e.g., executives, product managers, engineers, data scientists/analysts) to avoid the pitfalls and to make the most of A/B testing.More Detailed reading notesPart I is designed to be read by everyone, regardless of background. The basics about A/B testing (e.g., the concept of controlled experiments, the benefits) and the examples (Chapter 1 and Chapter 2) are relatively straightforward. The key message of Part I is that trustworthiness of (interpreting) experiment results is a critical issue. To increase the trust in experiments and experiment results, the authors laid out questions/suggestions from different levels:1. The company/executives level: three questions to answerDoes the company want to build a culture of making data-driven decisions?Is the company willing/ready to invest in the infrastructure and test to run A/B testing?Is the current way of assessing the value of ideas poor or not good enough?If yes, read the tenets (Page 11 to Page 14). More details in Chapters 4, 6, 7, and 8.2. The middle/design level (for senior manager, product managers)How to help build a robust and trustworthy experiment platform?How to design metrics and align them with missions/long-term goals (many goals contradict each other)?How to ensure that the results are trustworthy in general (internal validity and external validity)?Is A/B testing the only technique to provide evidence for data-driven decisions?Detailed answers are in Chapter 4 in Part I, Part II (Chapter 5 to Chapter 9) and Part III (Chapter 10 and Chapter 11).3. The operation level (for data analysts, engineers)The list of questions will be much longer and at a more detailed level. A few examples:How to process logs from multiple sources (e.g., logs from servers, logs from different client types)?How to choose a randomization unit (user level or session level)?How to conduct a power analysis?How to interpret the experiment results (e.g., p-value, confidence interval)?Details answers are in Part IV (Chapter 12 to Chapter 16) and Part V (Chapter 17 to Chapter 23).In sum, Part I introduces the basics about A/B testing and lays out the framework for the rest of the book. 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