Сравнение на методи

	Експериментален дизайн с двойно заслепяване, предварителен и последващ тест	Експериментален дизайн с контролна група
Област	Планиране на експеримента	Планиране на експеримента
Семейство	Process / pipeline	Process / pipeline
Година на възникване	Mid-20th century (combined form widely adopted 1960s onward)	1935 (Fisher); 1963 (Campbell & Stanley codification)
Създател	Campbell & Stanley (formalized pretest-posttest design, 1963); double-blind blinding convention developed in clinical pharmacology (19th-20th century)	Ronald A. Fisher; systematised by Donald T. Campbell & Julian C. Stanley
Тип	True experimental design	Experimental research design
Основополагащ източник	Campbell, D. T., & Stanley, J. C. (1963). Experimental and quasi-experimental designs for research. In N. L. Gage (Ed.), Handbook of Research on Teaching (pp. 171-246). Rand McNally.	Campbell, D. T., & Stanley, J. C. (1963). Experimental and Quasi-Experimental Designs for Research. Rand McNally.
Други названия	DB-pretest-posttest design, double-blind pre-post design, masked pretest-posttest RCT, double-masked pre-post experiment	controlled experiment, true experimental design, randomized controlled design, treatment-control design
Свързани	5	4
Резюме	The double-blind pretest-posttest experimental design is a true experiment in which participants are randomly assigned to treatment and control conditions, outcome data are collected both before and after the intervention, and neither participants nor outcome assessors know which condition each participant received. Combining baseline measurement with strong blinding, the design controls for both pre-existing group differences and expectancy-driven bias, making it a gold-standard approach in clinical and behavioral research.	Control group experimental design is a fundamental experimental structure in which participants are assigned to at least two groups — a treatment group that receives the intervention and a control group that does not — so that the effect of the intervention can be isolated by comparing outcomes across groups. Randomisation of assignment strengthens causal inference by balancing known and unknown confounders.