Compara mètodes
Revisa els mètodes seleccionats l'un al costat de l'altre; les files que difereixen es ressalten.
| Prova A/B adaptativa× | Test A/B bloquejat× | |
|---|---|---|
| Camp | Disseny experimental | Disseny experimental |
| Família | Process / pipeline | Process / pipeline |
| Any d'origen≠ | 1952 (Robbins); applied to A/B testing from ~2010s onward | 1926 (blocking principle); 2000s–2010s (online A/B testing application) |
| Autor original≠ | Herbert Robbins (bandit framework); Thompson Sampling formalized by William R. Thompson | R. A. Fisher (blocking principle); adapted to online A/B testing by industry practitioners |
| Tipus≠ | Adaptive experimental design | Randomized controlled experiment with variance reduction |
| Font seminal≠ | 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 ↗ | Fisher, R. A. (1926). The arrangement of field experiments. Journal of the Ministry of Agriculture of Great Britain, 33, 503–513. link ↗ |
| Àlies | adaptive AB test, bandit A/B test, multi-armed bandit testing, online adaptive experiment | block-randomized A/B test, stratified A/B test, blocked split test, block-design A/B experiment |
| Relacionats≠ | 6 | 4 |
| Resum≠ | 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 blocked A/B test is an experimental design that partitions units (users, subjects, or clusters) into homogeneous blocks before randomly assigning them to treatment A or treatment B within each block. Blocking reduces within-experiment noise by ensuring that known sources of variation — such as device type, geography, or user tenure — are balanced across conditions, yielding more precise estimates of the treatment effect than a simple unblocked A/B test. |
| ScholarGateConjunt de dades ↗ |
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