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

This practical guide for students, researchers and practitioners offers real world guidance for data-driven decision making and innovation.

Ron Kohavi (Author), Diane Tang (Author), Ya Xu (Author)

9781108724265, Cambridge University Press

Paperback / softback, published 2 April 2020

288 pages, 21 b/w illus.
22.6 x 15.2 x 1.4 cm, 0.4 kg

'Experimentation is the future of digital strategy and 'Trustworthy Experiments' will be its Bible. Kohavi, Tang and Xu are three of the most noteworthy experts on experimentation working today and their book delivers a truly practical roadmap for digital experimentation that is useful right out of the box. The revealing case studies they conducted over many decades at Microsoft, Amazon, Google and LinkedIn are organized into easy to understand practical lessens with tremendous depth and clarity. It should be required reading for any manager of a digital business.' Sinan Aral, David Austin Professor of Management, Massachusetts Institute of Technology, and author of The Hype Machine

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.

Preface – how to read this book
1. Introduction and motivation
2. Running and analyzing experiments: an end-to-end example
3. Twyman's law and experimentation trustworthiness
4. Experimentation platform and culture
Part II: 5. Speed matters: an end-to-end case study
6. Organizational metrics
7. Metrics for experimentation and the Overall Evaluation Criterion (OEC)
8. Institutional memory and aeta-analysis
9. Ethics in controlled experiments
Part III: 10. Complementary techniques
11. Observational causal studies
Part IV: 12. Client-side experiments
13. Instrumentation
14. Choosing a randomization unit
15. Ramping experiment exposure: trading off speed, quality, and risk
16. Scaling experiment analyses
Part V: 17. The statistics behind online controlled experiments
18. Variance estimation and improved sensitivity: pitfalls and solutions
19. The A/A test
20. Triggering for improved sensitivity
21. Guardrail metrics
22. Leakage and interference between variants
23. Measuring long-term treatment effects.

Subject Areas: Mathematical theory of computation [UYA], Data mining [UNF], Market research [KJSM], Knowledge management [KJMV3]