방법 비교

	단일 눈가림 실험실 실험	이중 눈가림 실험실 실험
분야	실험설계	실험설계
계열	Process / pipeline	Process / pipeline
기원 연도	Late 19th century; codified in 20th-century clinical and behavioral research	Mid-20th century (widespread adoption ~1950s onward)
창시자	Formalized in experimental psychology and pharmacology; Peirce & Jastrow (1884) early instance	Rooted in 19th-century pharmacological and psychological research traditions; systematized in clinical and experimental science through the 20th century
유형	Controlled experimental design	Controlled experimental design with blinding
원전	Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Houghton Mifflin. ISBN: 978-0395615560	Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Houghton Mifflin. ISBN: 978-0395615560
별칭	single-masked laboratory study, participant-blind lab experiment, single-blind controlled lab study	double-blind lab experiment, double-masked laboratory experiment, DB lab experiment, double-blind controlled lab study
관련	5	5
요약	A single-blind laboratory experiment is a controlled study conducted in a laboratory setting in which participants do not know which condition (e.g., treatment or control) they have been assigned to, while the researchers administering the conditions are aware. This masking of participants reduces demand characteristics and response bias without requiring full investigator blinding, and the controlled laboratory environment allows tight manipulation of independent variables and precise measurement of outcomes.	A double-blind laboratory experiment is a controlled experimental design conducted in a laboratory setting in which neither the participants nor the researchers directly administering the treatment know which condition each participant has been assigned to. This dual blinding, combined with the high degree of environmental control characteristic of laboratory settings, minimizes both participant expectancy effects and experimenter bias, making it one of the most rigorous designs available for isolating causal relationships between independent and dependent variables.