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Esamina i metodi selezionati fianco a fianco; le righe che differiscono sono evidenziate.
| Test A/B Adattivo× | Test fattoriale A/B× | |
|---|---|---|
| Campo | Disegno sperimentale | Disegno sperimentale |
| Famiglia | Process / pipeline | Process / pipeline |
| Anno di origine≠ | 1952 (Robbins); applied to A/B testing from ~2010s onward | Factorial design: 1920s–1930s; applied online as factorial A/B test: 2000s–2010s |
| Ideatore≠ | Herbert Robbins (bandit framework); Thompson Sampling formalized by William R. Thompson | Ronald A. Fisher (factorial design); digital A/B testing popularized by Google, Microsoft, and Amazon in the 2000s |
| Tipo≠ | Adaptive experimental design | Controlled online/field experiment |
| Fonte seminale≠ | Russo, D., Van Roy, B., Kazerouni, A., Osband, I., & Wen, Z. (2018). A Tutorial on Thompson Sampling. Foundations and Trends in Machine Learning, 11(1), 1–96. DOI ↗ | Kohavi, R., Tang, D., & Xu, Y. (2020). Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing. Cambridge University Press. ISBN: 978-1108724265 |
| Alias | adaptive AB test, bandit A/B test, multi-armed bandit testing, online adaptive experiment | factorial split test, multi-factor A/B test, factorial online experiment, factorial controlled experiment |
| Correlati | 6 | 6 |
| Sintesi≠ | An Adaptive A/B test is an experimental design that dynamically reallocates traffic or participants toward better-performing variants during the experiment itself, rather than holding allocations fixed until the end. Drawing on multi-armed bandit algorithms such as Thompson Sampling or Upper Confidence Bound (UCB), it balances the exploration of uncertain variants with the exploitation of those already showing superior performance, typically yielding higher aggregate outcomes while still producing valid inferential conclusions. | A factorial A/B test is a controlled online experiment that simultaneously manipulates two or more independent factors, each at two or more levels, exposing different user groups to every combination of factor levels. Rooted in Fisher's factorial design and operationalised at scale by tech companies, it enables researchers to estimate both the independent main effect of each factor and the interaction effects between factors — all from a single experimental run. |
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