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 committee.
Based 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
