9781108724265-1108724264-Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing

Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing

ISBN-13: 9781108724265
ISBN-10: 1108724264
Edition: 1
Author: Ron Kohavi, Diane Tang, Ya Xu
Publication date: 2020
Publisher: Cambridge University Press
Format: Paperback 288 pages
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Book details

ISBN-13: 9781108724265
ISBN-10: 1108724264
Edition: 1
Author: Ron Kohavi, Diane Tang, Ya Xu
Publication date: 2020
Publisher: Cambridge University Press
Format: Paperback 288 pages

Summary

Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing (ISBN-13: 9781108724265 and ISBN-10: 1108724264), written by authors Ron Kohavi, Diane Tang, Ya Xu, was published by Cambridge University Press in 2020. With an overall rating of 4.3 stars, it's a notable title among other Data Mining (Databases & Big Data) books. You can easily purchase or rent Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing (Paperback) from BooksRun, along with many other new and used Data Mining books and textbooks. And, if you're looking to sell your copy, our current buyback offer is $11.8.

Description

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. 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 * 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 experimenters to 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.

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