Krahasoni metodat

	Test A/B i bllokuar	Test A/B Faktoriel
Fusha	Dizajni eksperimental	Dizajni eksperimental
Familja	Process / pipeline	Process / pipeline
Viti i origjinës	1926 (blocking principle); 2000s–2010s (online A/B testing application)	Factorial design: 1920s–1930s; applied online as factorial A/B test: 2000s–2010s
Krijuesi	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
Lloji	Randomized controlled experiment with variance reduction	Controlled online/field experiment
Burimi themelues	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
Emërtime të tjera	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
Të lidhura	4	6
Përmbledhja	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.