Porovnat metody

	Faktoriální A/B test	Experiment s více rameny
Obor	Plánování experimentů	Plánování experimentů
Rodina	Process / pipeline	Process / pipeline
Rok vzniku	Factorial design: 1920s–1930s; applied online as factorial A/B test: 2000s–2010s	1990s–2000s (clinical formalization); multi-arm concept implicit in ANOVA-era factorial designs
Tvůrce	Ronald A. Fisher (factorial design); digital A/B testing popularized by Google, Microsoft, and Amazon in the 2000s	Developed within clinical trials methodology; formalized by Parmar, Royston and colleagues (UK MRC CTU, early 2000s)
Typ	Controlled online/field experiment	Experimental design
Původní zdroj	Kohavi, R., Tang, D., & Xu, Y. (2020). Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing. Cambridge University Press. ISBN: 978-1108724265	Royston, P., Parmar, M. K. B., & Qian, W. (2003). Novel designs for multi-arm clinical trials with survival outcomes with an application in ovarian cancer. Statistics in Medicine, 22(14), 2239–2256.
Další názvy	factorial split test, multi-factor A/B test, factorial online experiment, factorial controlled experiment	multi-arm trial, multiple-arm experiment, multi-group experiment, many-arm design
Příbuzné	6	5
Shrnutí	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.	A multi-arm experiment simultaneously compares three or more treatment or intervention conditions — each called an arm — against a shared control or against one another. By testing multiple alternatives in a single study, it yields more information per participant than running separate two-group experiments sequentially, while controlling the overall Type I error rate through pre-specified comparison strategies.