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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比较可惜，guideline偏多，完整例子较少（或许可直接看引用的论文？）。第2, 7 章还算不错，Part IV 平台建设有些太简单了，给内部平台的截图可能更好（因此甚至不如netflix等的blog）；Part V 讲一些高级的统计分析方法，以后可以读. 简单来说，适合data scientist在做试验的时候翻阅，不适合没有经验的人入门、深入学习
评分A/B testing比网络瞎讲的tutorial好太多了
评分A/B testing 从入门到精通
评分实验评估部分尤其好
评分实验评估部分尤其好
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