Σύγκριση μεθόδων

	Πιλοτικό πείραμα A/B	Factorial A/B Test
Πεδίο	Πειραματικός Σχεδιασμός	Πειραματικός Σχεδιασμός
Οικογένεια	Process / pipeline	Process / pipeline
Έτος προέλευσης	2000s–2010s (formalized in digital experimentation literature)	Factorial design: 1920s–1930s; applied online as factorial A/B test: 2000s–2010s
Δημιουργός	Derived from pilot study methodology (Kraemer et al., 2006) applied to A/B testing practice	Ronald A. Fisher (factorial design); digital A/B testing popularized by Google, Microsoft, and Amazon in the 2000s
Τύπος	Experimental design — feasibility study	Controlled online/field experiment
Θεμελιώδης πηγή	Thabane, L., Ma, J., Chu, R., Cheng, J., Ismaila, A., Rios, L. P., Robson, R., Thabane, M., Giangregorio, L., & Goldsmith, C. H. (2010). A tutorial on pilot studies: The what, why and how. BMC Medical Research Methodology, 10(1), 1.	Kohavi, R., Tang, D., & Xu, Y. (2020). Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing. Cambridge University Press. ISBN: 978-1108724265
Εναλλακτικές ονομασίες	pilot split test, feasibility A/B test, preliminary A/B experiment, pilot randomized comparison	factorial split test, multi-factor A/B test, factorial online experiment, factorial controlled experiment
Συναφείς	5	6
Σύνοψη	A Pilot A/B test is a small-scale, preliminary split-test experiment run before a full A/B test to assess feasibility, estimate effect sizes, detect operational problems, and validate measurement instruments. Participants are randomly assigned to a control condition (A) and a treatment condition (B), but the study is explicitly underpowered — its purpose is to inform the design of the definitive test, not to yield a conclusive comparison.	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.