方法对比
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| 实用型 A/B 测试× | A/B 测试（在线对照实验）× | |
|---|---|---|
| 领域 | 实验设计 | 实验设计 |
| 方法族≠ | Process / pipeline | Hypothesis test |
| 起源年份≠ | 1967 (pragmatic framing); 2007–2012 (large-scale tech A/B testing practice) | 1935 |
| 提出者≠ | Pragmatic trial framing: Schwartz & Lellouch (1967); A/B testing in technology: Ron Kohavi and colleagues at Microsoft (~2007–2012) | Ron Kohavi et al. (Microsoft); conceptual roots in R. A. Fisher's randomized experiments (1935) |
| 类型≠ | Randomized comparative experiment | Parametric comparison (frequentist or Bayesian) |
| 开创性文献≠ | Schwartz, D., & Lellouch, J. (1967). Explanatory and pragmatic attitudes in therapeutical trials. Journal of Chronic Diseases, 20(8), 637–648. DOI ↗ | Kohavi, R., Tang, D., & Xu, Y. (2020). Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing. Cambridge University Press. ISBN: 9781108724265 |
| 别名 | pragmatic split test, real-world A/B experiment, pragmatic online experiment, pragmatic controlled experiment | split test, controlled experiment, two-variant test, A/B Testi (Online Kontrollü Deney) |
| 相关≠ | 3 | 4 |
| 摘要≠ | A pragmatic A/B test is a randomized comparative experiment that evaluates two alternatives — a control (A) and a treatment (B) — under real-world operating conditions rather than tightly controlled laboratory settings. Rooted in the pragmatic-versus-explanatory trial distinction introduced by Schwartz and Lellouch in 1967 and brought to large-scale practice by online experimentation teams at Microsoft, Google, and Amazon, it prioritizes decision-relevant effectiveness over internal mechanistic explanation. | An A/B test is a randomized controlled experiment that simultaneously exposes two groups of users to a control variant (A) and a treatment variant (B) in order to determine whether a measured outcome differs significantly between them. The modern online controlled experiment framework was systematized by Ron Kohavi and colleagues at Microsoft in the early 2000s, building on R. A. Fisher's classical randomization principles from 1935. It is the dominant causal inference tool in web product development, digital marketing, and experimentation platforms. |
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