Trustworthy Online Controlled Experiments - Ron Kohavi, Diane Tang, Ya Xu

Trustworthy Online Controlled Experiments

Ron Kohavi, Diane Tang, Ya Xu

出版时间

2020-05-01

ISBN

9781108724265

评分

★★★★★
书籍介绍
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.
精彩摘录
  • "1. The randomization unit is a user. 2. We will target all users and analyze those who visit the checkout page. 3. To have 80% power to detect at least a 1% change in revenue-per-user, we will conduct a power analysis to determine size. Designing the Experiment 334. This translates into running the "
用户评论
8/10.
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
A/B testing 从入门到精通
实验评估部分尤其好
是三位作者完整的业界实践,涵盖工程产品等方方面面,不偏统计理论。本书像字典一样,从业者在ab测试过程中遇到的很多问题都可以从书里面获得解答与启发。随着读者自身对实验的深入理解,阅读此书会有常读常新的感觉
不是入门书,但可快速了解实战case。
读完还是挺失望的,可能是已经从业了比较久的原因。整体来说,更像是一本入门的书籍,在工具和方法的介绍上比较全面,比如反转实验、PSM等等,但是比较缺乏深入的东西,实验设计和解释分析其实比较重要,实验设计一旦失败,影响是巨大的。
三百页实操干货,是企业级运用里的葵花宝典了。
结构比较全面,但要深不深,要浅不浅。想要深入学习还得看参考文献,例子也不够多。当个概览目录还行
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