Σύγκριση μεθόδων

	Πραγματιστική Δοκιμή A/B	Πειραματικός Σχεδιασμός Πλήρους Παραγοντικού Τύπου
Πεδίο	Πειραματικός Σχεδιασμός	Πειραματικός Σχεδιασμός
Οικογένεια	Process / pipeline	Hypothesis test
Έτος προέλευσης	1967 (pragmatic framing); 2007–2012 (large-scale tech A/B testing practice)	1926
Δημιουργός	Pragmatic trial framing: Schwartz & Lellouch (1967); A/B testing in technology: Ron Kohavi and colleagues at Microsoft (~2007–2012)	R. A. Fisher
Τύπος	Randomized comparative experiment	Parametric factorial experiment
Θεμελιώδης πηγή	Schwartz, D., & Lellouch, J. (1967). Explanatory and pragmatic attitudes in therapeutical trials. Journal of Chronic Diseases, 20(8), 637–648.	Box, G. E. P., Hunter, J. S., & Hunter, W. G. (2005). Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.). Wiley. ISBN: 978-0471718130
Εναλλακτικές ονομασίες	pragmatic split test, real-world A/B experiment, pragmatic online experiment, pragmatic controlled experiment	factorial experiment, 2^k factorial, full factorial, Faktöriyel Deneme Deseni (Full Factorial, 2^k)
Συναφείς	3	5
Σύνοψη	A pragmatic A/B test is a randomized comparative experiment that evaluates two alternatives — a control (A) and a treatment (B) — under real-world operating conditions rather than tightly controlled laboratory settings. Rooted in the pragmatic-versus-explanatory trial distinction introduced by Schwartz and Lellouch in 1967 and brought to large-scale practice by online experimentation teams at Microsoft, Google, and Amazon, it prioritizes decision-relevant effectiveness over internal mechanistic explanation.	A full factorial design is a parametric experimental method in which every combination of factor levels is tested simultaneously, enabling the estimation of all main effects and all interaction effects in a single study. Rooted in R. A. Fisher's foundational work on designed experiments (1926) and systematically developed by Box, Hunter, and Hunter (2005) and Montgomery (2017), the 2^k form tests k two-level factors across 2^k experimental runs and is the benchmark against which all other factorial designs are measured.