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| A/B Test (Online Kontrolleret Eksperiment)× | Adaptivt klinisk forsøgsdesign× | |
|---|---|---|
| Fagområde | Forsøgsdesign | Forsøgsdesign |
| Familie | Hypothesis test | Hypothesis test |
| Oprindelsesår≠ | 1935 | 1994 |
| Ophavsperson≠ | Ron Kohavi et al. (Microsoft); conceptual roots in R. A. Fisher's randomized experiments (1935) | Bauer & Köhne |
| Type≠ | Parametric comparison (frequentist or Bayesian) | Adaptive hypothesis test with interim analyses |
| Oprindelig kilde≠ | Kohavi, R., Tang, D., & Xu, Y. (2020). Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing. Cambridge University Press. ISBN: 9781108724265 | Bauer, P. & Köhne, K. (1994). Evaluation of Experiments with Adaptive Interim Analyses. Biometrics, 50(4), 1029–1041. DOI ↗ |
| Aliasser≠ | split test, controlled experiment, two-variant test, A/B Testi (Online Kontrollü Deney) | adaptive design, group sequential design, sample size re-estimation, platform trial |
| Relaterede≠ | 4 | 3 |
| Resumé≠ | 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. | Adaptive clinical trial design is a flexible experimental framework, formalised by Bauer and Köhne in 1994, in which pre-specified rules allow the trial to be modified mid-course — adjusting sample size, treatment arms, or randomisation ratios — based on accumulating interim data while rigorously controlling the Type I error rate. |
| ScholarGateDatasæt ↗ |
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