Methoden vergleichen

	Faktorieller A/B-Test	Faktorielles Experiment
Fachgebiet	Versuchsplanung	Versuchsplanung
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	Factorial design: 1920s–1930s; applied online as factorial A/B test: 2000s–2010s	1926–1935
Urheber	Ronald A. Fisher (factorial design); digital A/B testing popularized by Google, Microsoft, and Amazon in the 2000s	Ronald A. Fisher
Typ	Controlled online/field experiment	Quantitative experimental design
Wegweisende Quelle	Kohavi, R., Tang, D., & Xu, Y. (2020). Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing. Cambridge University Press. ISBN: 978-1108724265	Fisher, R. A. (1935). The Design of Experiments. Oliver and Boyd.
Aliasnamen	factorial split test, multi-factor A/B test, factorial online experiment, factorial controlled experiment	factorial design, factorial ANOVA design, multi-factor experiment, crossed-factor design
Verwandt	6	6
Zusammenfassung	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 factorial experiment is an experimental design in which two or more independent variables (factors) are manipulated simultaneously, and every combination of their levels is tested. Introduced by Ronald Fisher in the 1920s–1930s, it is the standard approach whenever a researcher needs to detect not only the main effect of each factor but also whether the effect of one factor depends on the level of another — the interaction effect.