Comparar métodos

	Prueba A/B Adaptativa	Diseño Experimental Adaptativo
Campo	Diseño experimental	Diseño experimental
Familia	Process / pipeline	Process / pipeline
Año de origen	1952 (Robbins); applied to A/B testing from ~2010s onward	1940s–1970s (sequential foundations); formalised in clinical and behavioural research by 1980s–2000s
Autor original	Herbert Robbins (bandit framework); Thompson Sampling formalized by William R. Thompson	Abraham Wald (sequential analysis foundation); expanded by Robbins, Armitage, and others
Tipo	Adaptive experimental design	Experimental research design
Fuente 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.	Chow, S. C., & Chang, M. (2008). Adaptive Design Methods in Clinical Trials. Chapman and Hall/CRC. ISBN: 978-1584886761
Alias	adaptive AB test, bandit A/B test, multi-armed bandit testing, online adaptive experiment	adaptive design, response-adaptive randomization, adaptive trial, adaptive randomization
Relacionados	6	5
Resumen	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.	An adaptive experiment is an experimental design in which pre-specified rules allow the protocol to be modified — such as reallocating participants to better-performing arms, stopping early for efficacy or futility, or changing sample size — based on accumulating interim data, while maintaining statistical validity. Adaptive designs are widely used in clinical trials, behavioural economics, and online platform testing to improve efficiency and ethics without sacrificing inferential rigour.