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

כריכה קדמית
Cambridge University Press, 2 באפר׳ 2020 - 288 עמודים
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.
 

תוכן

Introduction and Motivation
3
Necessary Ingredients for Running Useful Controlled Experiments
10
Examples of Interesting Online Controlled Experiments
16
Additional Reading
24
Designing the Experiment
32
Twymans Law and Experimentation Trustworthiness
39
Threats to External Validity
48
Experimentation Platform and Culture
58
advanced topics for building
151
Instrumentation
162
14
166
Trading Off Speed
171
16
177
advanced topics for analyzing
183
Pitfalls
193
The AA Test
200

selected topics for everyone
79
Organizational Metrics
90
Metrics for Experimentation and the Overall
102
Institutional Memory and MetaAnalysis
111
techniques to controlled experiments
125
10
127
Observational Causal Studies
137
20
209
Sample Ratio Mismatch and Other TrustRelated
219
Leakage and Interference between Variants
226
Measuring LongTerm Treatment Effects
235
References
246
Index
266
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מידע על המחבר (2020)

Ron Kohavi is a Technical Fellow and corporate VP of Microsoft's Analysis and Experimentation, and was previously director of data mining and personalization at Amazon. He received his Ph.D. in Computer Science from Stanford University. His papers have over 40,000 citations and three of them are in the top 1,000 most-cited papers in Computer Science. Diane Tang is a Google Fellow, with expertise in large-scale data analysis and infrastructure, online controlled experiments, and ads systems. She has an A.B. from Harvard and an M.S./Ph.D. from Stanford University, with patents and publications in mobile networking, information visualization, experiment methodology, data infrastructure, data mining, and large data. Ya Xu heads Data Science and Experimentation at LinkedIn. She has published several papers on experimentation and is a frequent speaker at top-tier conferences and universities. She previously worked at Microsoft and received her Ph.D. in Statistics from Stanford University.

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