방법 비교

	단일 눈가림 A/B 테스트	이중 눈가림 A/B 테스트
분야	실험설계	실험설계
계열	Process / pipeline	Process / pipeline
기원 연도	mid-20th century (blinded RCT framework); A/B test nomenclature ~1990s–2000s	1935 (Fisher's formal randomized design); double-blinding in A/B testing: 1990s–2000s
창시자	Fisher, R. A. (randomisation basis); blinding practice formalised in clinical trials mid-20th century	Evolved from clinical trial methodology; early systematic blinding attributed to James Lind (1753) and formalized by R. A. Fisher (1935)
유형	Controlled experiment with partial blinding	Randomized controlled experiment with blinding
원전	Kohavi, R., Tang, D., & Xu, Y. (2020). Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing. Cambridge University Press. ISBN: 978-1108724265	Schulz, K. F., Altman, D. G., & Moher, D. (2010). CONSORT 2010 Statement: Updated guidelines for reporting parallel group randomised trials. BMJ, 340, c332.
별칭	single-masked A/B test, single-blind split test, blinded two-condition experiment, participant-blind A/B test	double-blind split test, double-blinded A/B experiment, blinded two-arm randomized experiment, double-blind controlled A/B trial
관련	6	5
요약	A single-blind A/B test is a controlled two-condition experiment in which participants are randomised to condition A (control) or condition B (treatment) but are kept unaware of which condition they have received, while researchers and analysts remain aware. The blind prevents participants from changing their behaviour in response to knowledge of their assignment, reducing demand characteristics and response bias while still allowing the investigator to monitor the trial.	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.