Sammenlign metoder

	Blokkert A/B-test	Faktoriell A/B-test
Fagfelt	Forsøksdesign	Forsøksdesign
Familie	Process / pipeline	Process / pipeline
Opprinnelsesår	1926 (blocking principle); 2000s–2010s (online A/B testing application)	Factorial design: 1920s–1930s; applied online as factorial A/B test: 2000s–2010s
Opphavsperson	R. A. Fisher (blocking principle); adapted to online A/B testing by industry practitioners	Ronald A. Fisher (factorial design); digital A/B testing popularized by Google, Microsoft, and Amazon in the 2000s
Type	Randomized controlled experiment with variance reduction	Controlled online/field experiment
Opprinnelig kilde	Fisher, R. A. (1926). The arrangement of field experiments. Journal of the Ministry of Agriculture of Great Britain, 33, 503–513.	Kohavi, R., Tang, D., & Xu, Y. (2020). Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing. Cambridge University Press. ISBN: 978-1108724265
Alias	block-randomized A/B test, stratified A/B test, blocked split test, block-design A/B experiment	factorial split test, multi-factor A/B test, factorial online experiment, factorial controlled experiment
Relaterte	4	6
Sammendrag	A blocked A/B test is an experimental design that partitions units (users, subjects, or clusters) into homogeneous blocks before randomly assigning them to treatment A or treatment B within each block. Blocking reduces within-experiment noise by ensuring that known sources of variation — such as device type, geography, or user tenure — are balanced across conditions, yielding more precise estimates of the treatment effect than a simple unblocked A/B test.	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.