방법 비교

	요인 분할 A/B 테스트	일부 요인 실험
분야	실험설계	실험설계
계열	Process / pipeline	Process / pipeline
기원 연도	Factorial design: 1920s–1930s; applied online as factorial A/B test: 2000s–2010s	1945 (Finney); broader development 1950s–1970s by Box, Hunter
창시자	Ronald A. Fisher (factorial design); digital A/B testing popularized by Google, Microsoft, and Amazon in the 2000s	D. J. Finney (formal development); foundations in Ronald Fisher's factorial design work
유형	Controlled online/field experiment	Quantitative experimental design
원전	Kohavi, R., Tang, D., & Xu, Y. (2020). Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing. Cambridge University Press. ISBN: 978-1108724265	Box, G. E. P., Hunter, J. S., & Hunter, W. G. (2005). Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.). Wiley-Interscience. ISBN: 978-0471718130
별칭	factorial split test, multi-factor A/B test, factorial online experiment, factorial controlled experiment	fractional factorial design, FFD, 2^(k-p) design, fractional replication
관련	6	4
요약	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 fractional factorial experiment is a resource-efficient experimental design that tests only a carefully chosen fraction of all possible factor-level combinations. By exploiting the principle that high-order interactions are usually negligible, it identifies the main effects and low-order interactions of k factors using far fewer runs than a full factorial design — making it the workhorse of industrial and engineering screening experiments.