Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| A/B-тест (онлайн-контролируемый эксперимент)× | Независимый t-критерий для двух выборок× | |
|---|---|---|
| Область≠ | Планирование эксперимента | Статистика |
| Семейство | Hypothesis test | Hypothesis test |
| Год появления≠ | 1935 | 1908 |
| Автор метода≠ | Ron Kohavi et al. (Microsoft); conceptual roots in R. A. Fisher's randomized experiments (1935) | Student (W. S. Gosset) |
| Тип≠ | Parametric comparison (frequentist or Bayesian) | Parametric mean comparison |
| Основополагающий источник≠ | Kohavi, R., Tang, D., & Xu, Y. (2020). Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing. Cambridge University Press. ISBN: 9781108724265 | Student (1908). The probable error of a mean. Biometrika, 6(1), 1–25. DOI ↗ |
| Другие названия | split test, controlled experiment, two-variant test, A/B Testi (Online Kontrollü Deney) | student t-test, two-sample t-test, unpaired t-test, bağımsız örneklem t-testi |
| Связанные | 4 | 4 |
| Сводка≠ | 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. | The independent samples t-test is a parametric hypothesis test that compares the means of two independent groups to decide whether they differ significantly. It builds on the t-distribution introduced by Student (W. S. Gosset) in 1908 and assumes the measured values are continuous, approximately normally distributed, and have equal variances. |
| ScholarGateНабор данных ↗ |
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