Trustworthy online controlled experiments a practical guide to A/B testing

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 th...

Full description

Bibliographic Details
Main Authors: Kohavi, Ron, Tang, Diane (Author), Xu, Ya (Author)
Format: eBook
Language:English
Published: Cambridge Cambridge University Press 2020
Subjects:
Online Access:
Collection: Cambridge Books Online - Collection details see MPG.ReNa
LEADER 01905nmm a2200265 u 4500
001 EB001901426
003 EBX01000000000000001064335
005 00000000000000.0
007 cr|||||||||||||||||||||
008 200915 ||| eng
020 |a 9781108653985 
050 4 |a HM741 
100 1 |a Kohavi, Ron 
245 0 0 |a Trustworthy online controlled experiments  |b a practical guide to A/B testing  |c Ron Kohavi, Diane Tang, Ya Xu 
260 |a Cambridge  |b Cambridge University Press  |c 2020 
300 |a xviii, 271 pages  |b digital 
653 |a Social media 
653 |a User-generated content / Social aspects 
700 1 |a Tang, Diane  |e [author] 
700 1 |a Xu, Ya  |e [author] 
041 0 7 |a eng  |2 ISO 639-2 
989 |b CBO  |a Cambridge Books Online 
856 4 0 |u https://doi.org/10.1017/9781108653985  |x Verlag  |3 Volltext 
082 0 |a 302.231 
520 |a 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