Σύγκριση μεθόδων

	Factorial A/B Test	Adaptive A/B Test
Πεδίο	Πειραματικός Σχεδιασμός	Πειραματικός Σχεδιασμός
Οικογένεια	Process / pipeline	Process / pipeline
Έτος προέλευσης	Factorial design: 1920s–1930s; applied online as factorial A/B test: 2000s–2010s	1952 (Robbins); applied to A/B testing from ~2010s onward
Δημιουργός	Ronald A. Fisher (factorial design); digital A/B testing popularized by Google, Microsoft, and Amazon in the 2000s	Herbert Robbins (bandit framework); Thompson Sampling formalized by William R. Thompson
Τύπος	Controlled online/field experiment	Adaptive experimental design
Θεμελιώδης πηγή	Kohavi, R., Tang, D., & Xu, Y. (2020). Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing. Cambridge University Press. ISBN: 978-1108724265	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.
Εναλλακτικές ονομασίες	factorial split test, multi-factor A/B test, factorial online experiment, factorial controlled experiment	adaptive AB test, bandit A/B test, multi-armed bandit testing, online adaptive experiment
Συναφείς	6	6
Σύνοψη	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.	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.