방법 비교

	Crossover A/B Test	요인 분할 A/B 테스트
분야	실험설계	실험설계
계열	Process / pipeline	Process / pipeline
기원 연도	1949 (crossover design); 2000s (online A/B application)	Factorial design: 1920s–1930s; applied online as factorial A/B test: 2000s–2010s
창시자	Crossover design: E. J. Williams (1949); A/B testing framework: Ronald Fisher (experimental roots); modern online application widely attributed to Google and Microsoft experimentation teams	Ronald A. Fisher (factorial design); digital A/B testing popularized by Google, Microsoft, and Amazon in the 2000s
유형	Within-subject controlled experiment	Controlled online/field experiment
원전	Jones, B., & Kenward, M. G. (2014). Design and Analysis of Cross-Over Trials (3rd ed.). Chapman and Hall/CRC. ISBN: 9781439861424	Kohavi, R., Tang, D., & Xu, Y. (2020). Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing. Cambridge University Press. ISBN: 978-1108724265
별칭	within-subject A/B test, crossover split test, repeated-measures A/B test, AB crossover experiment	factorial split test, multi-factor A/B test, factorial online experiment, factorial controlled experiment
관련	6	6
요약	A crossover A/B test is an experimental design in which the same participants or units are exposed to both treatment A and treatment B in sequence, with each serving as their own control. By eliminating between-subject variability, the design achieves higher statistical power than a standard parallel A/B test at the same sample size, but it requires careful handling of carryover effects and time-period confounds.	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.