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Statistical Methods in Online A/B Testing: Statistics for data-driven business decisions and risk management in e-commerce
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About Statistical Methods In Online A/B
Product Description "Statistical Methods in Online A/B Testing" is a comprehensive guide to statistics in online controlled experiments, a.k.a. A/B tests, that tackles the difficult matter of statistical inference in a way accessible to readers with little to no prior experience with it. Each concept is built from the ground up, explained thoroughly, and illustrated with practical examples from website testing. The presentation is straight to the point and practically oriented so you can apply the takeaways in your daily work.It is a must-read for anyone looking for a deep understanding of how to make data-driven business decisions through experimentation: conversion rate optimizers, product managers, growth experts, data analysts, marketing managers, experts in user experience and design. The new research presented and the fresh perspective on how to apply statistics and experimentation to achieve business goals make for an interesting read even for experienced statisticians.The book deals with scientific methods, but their introductions and explanations are grounded in the business goals they help achieve, such as innovating under controlled risk, and estimating the effect of proposed business actions before committing to them. While the book doesn't shy away from math and formulas, it is to the extent to which these are essential for understanding and applying the underlying concepts. The presentation is friendly to readers with little to no prior knowledge in statistics. Artificial and impractical examples like dice rolling and betting are absent, instead statistical concepts are illustrated through scenarios which might well be mistaken with the last couple of A/B tests you managed.This book also doesn't shy away from the fact that much of the current statistical theory and practice in online A/B testing is misguided, misinterpreted, or misapplied. It also addresses the issue of blind copying of scientific applications without due consideration of the unique features of online business, which is widespread. The book will help you avoid these malpractices by explicitly pointing out frequent mistakes, while also helping you align your usage of statistics and experimentation with any business goals you might want to pursue. Review "If you're running online experiments, you absolutely need this book. It will help you avoid costly testing mistakes stemming from misuse and misunderstanding of statistics." - Peep Laja, Founder and Principal, CXL Institute "This is simultaneously the most comprehensive and the most accessible book on A/B testing statistics I've encountered. Most companies are running A/B tests now, but the level of statistical malpractice is surprisingly high. If you run tests, then, this book should be required reading. Read it, and then gift it to the rest of your team to read, and watch your program's maturity rise in front of your eyes." - Alex Birkett, Senior Growth Marketing Manager, HubSpot "Statistical Methods in Online A/B Testing transforms the often-complex area of statistical modeling into an understandable and useful framework for those will little or no math background. Starting from the ground up, Georgiev introduces the reader to important statistical concepts with the goal of making the science behind A/B testing interpretable and useful. CRO professionals who lack the statistical background necessary to properly setup, run and interpret A/B tests will find this book a must have tool in augmenting their base of knowledge." - Barry Garner, Sales Development Manager, SiteGainer "Finally, a comprehensive reference on statistics for my own industry! No longer do I need 8 different books on statistics that were each bought for only one or two chapters each, because Georgi has delivered a text whereby each and every chapter is applicable to success in my daily work. Thank you for making such a complex subject both interesting and easier to understand." - Philip Cross,
