השוואת שיטות

	מבחן A/B כפול-סמיות	ניסוי מרובה זרועות
תחום	תכנון ניסויים	תכנון ניסויים
משפחה	Process / pipeline	Process / pipeline
שנת המקור	1935 (Fisher's formal randomized design); double-blinding in A/B testing: 1990s–2000s	1990s–2000s (clinical formalization); multi-arm concept implicit in ANOVA-era factorial designs
הוגה השיטה	Evolved from clinical trial methodology; early systematic blinding attributed to James Lind (1753) and formalized by R. A. Fisher (1935)	Developed within clinical trials methodology; formalized by Parmar, Royston and colleagues (UK MRC CTU, early 2000s)
סוג	Randomized controlled experiment with blinding	Experimental design
מקור מכונן	Schulz, K. F., Altman, D. G., & Moher, D. (2010). CONSORT 2010 Statement: Updated guidelines for reporting parallel group randomised trials. BMJ, 340, c332.	Royston, P., Parmar, M. K. B., & Qian, W. (2003). Novel designs for multi-arm clinical trials with survival outcomes with an application in ovarian cancer. Statistics in Medicine, 22(14), 2239–2256.
כינויים	double-blind split test, double-blinded A/B experiment, blinded two-arm randomized experiment, double-blind controlled A/B trial	multi-arm trial, multiple-arm experiment, multi-group experiment, many-arm design
קשורות	5	5
תקציר	A double-blind A/B test is a randomized experiment that compares two variants — a control (A) and a treatment (B) — while concealing group assignment from both participants and those administering or assessing the experiment. Combining the causal isolation of randomized assignment with blinding on both sides eliminates expectation-driven bias from participants and evaluator bias from analysts or administrators, producing cleaner causal estimates of treatment effect.	A multi-arm experiment simultaneously compares three or more treatment or intervention conditions — each called an arm — against a shared control or against one another. By testing multiple alternatives in a single study, it yields more information per participant than running separate two-group experiments sequentially, while controlling the overall Type I error rate through pre-specified comparison strategies.