The book Trustworthy Online Controlled Experiments is a practical guide to A/B testing and online controlled experiments, written by experts from Google, LinkedIn, and Microsoft. It provides insights into accelerating innovation using trustworthy online controlled experiments, shares examples and pitfalls, and offers advice for both beginners and experienced practitioners.
The target audience for the book "Trustworthy Online Controlled Experiments" includes students and industry professionals getting started with experiments, experienced practitioners who want to improve their data-driven decision making, and a wider audience interested in online controlled experiments. It is particularly recommended for leaders, executives, data scientists, engineers, analysts, and product managers.
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Controlled experiments are crucial for data-driven decision making in organizations, enabling the testing of ideas and strategies, but must be designed properly and interpreted correctly to ensure trustworthy results.
A culture of experimentation, supported by a robust platform and leadership, accelerates innovation by facilitating reliable experiments and learning from past outcomes.
Experiments are crucial tools for measuring performance and progress, but they must be conducted ethically, with respect for participants, and with clear, actionable metrics that align with the organization's goals.
Research methods, including complementary techniques and observational causal studies, each have their strengths and limitations, and should be chosen based on the specific needs of the research, ensuring a well-rounded and reliable result.
The text emphasizes the importance of high-quality instrumentation in running server-side and client-side experiments, and fostering a culture of instrumentation for reliable data-driven decision making.
Designing and analyzing experiments requires careful consideration of the unit of randomization, gradual rollout of features, and effective data preparation and interpretation.
Statistical concepts like hypothesis testing, p-values, confidence intervals, and statistical power are essential tools for making evidence-based decisions, akin to detective tools in solving a case.
A/A testing is a critical process in validating an experimentation platform's accuracy and detecting potential issues, while the concept of triggering helps refine the analysis by focusing on affected users.
Guardrail metrics, such as the Sample Ratio Mismatch, are essential tools in experimentation to ensure valid results, detect errors, and monitor for interference or leakage in both direct and indirect connections.
Understanding and measuring the long-term impacts of product changes is complex but crucial, requiring thoughtful strategies to overcome challenges and gain valuable insights into user behavior and market dynamics.
"Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing" by Ron Kohavi, Diane Tang, and Ya Xu is a comprehensive guide to conducting online controlled experiments, or A/B tests. The authors, who are leaders in experimentation at Google, LinkedIn, and Microsoft, share their practical experiences and provide advice for both beginners and experienced practitioners. The book covers the basics of running online controlled experiments, common pitfalls, and how to build an experimentation platform. It also delves into advanced topics for experienced practitioners. The authors emphasize the importance of trustworthy results, encouraging readers to double-check results and run validity tests.
Ron Kohavi is a renowned computer scientist specializing in data mining, machine learning, and web analytics. Ya Xu, on the other hand, is a leading data scientist at LinkedIn, with expertise in statistical modeling, experimental design, and causal inference.
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