Porovnat metody

	Dvouklinový laboratorní experiment	Faktoriální laboratorní experiment
Obor	Plánování experimentů	Plánování experimentů
Rodina	Process / pipeline	Process / pipeline
Rok vzniku	Mid-20th century (widespread adoption ~1950s onward)	1926 (Fisher's factorial principle); laboratory application systematized mid-20th century
Tvůrce	Rooted in 19th-century pharmacological and psychological research traditions; systematized in clinical and experimental science through the 20th century	Ronald A. Fisher
Typ	Controlled experimental design with blinding	Experimental research design
Původní zdroj	Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Houghton Mifflin. ISBN: 978-0395615560	Kirk, R. E. (2013). Experimental Design: Procedures for the Behavioral Sciences (4th ed.). Sage Publications. ISBN: 978-1412974455
Další názvy	double-blind lab experiment, double-masked laboratory experiment, DB lab experiment, double-blind controlled lab study	factorial lab experiment, laboratory factorial design, factorial controlled experiment, multi-factor lab study
Příbuzné	5	2
Shrnutí	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.	A factorial laboratory experiment is a controlled experimental design in which two or more independent variables (factors) are simultaneously manipulated, each at two or more levels, within a laboratory setting. This design allows researchers to estimate both the individual main effect of each factor and the interaction effects between factors — making it one of the most efficient and informative designs in behavioral, psychological, and natural science research.