Porównaj metody

	Zaprojektowanie eksperymentalne z blokami i pomiarem przed i po interwencji	Projektowanie eksperymentalne z grupą kontrolną
Dziedzina	Planowanie eksperymentów	Planowanie eksperymentów
Rodzina	Process / pipeline	Process / pipeline
Rok powstania	1935 (blocking, Fisher); 1963 (pretest-posttest + blocking synthesis, Campbell & Stanley)	1935 (Fisher); 1963 (Campbell & Stanley codification)
Twórca	Donald T. Campbell & Julian C. Stanley (systematized); blocking technique from Ronald A. Fisher	Ronald A. Fisher; systematised by Donald T. Campbell & Julian C. Stanley
Typ	Experimental design	Experimental research design
Źródło pierwotne	Campbell, D. T., & Stanley, J. C. (1963). Experimental and Quasi-Experimental Designs for Research. Rand McNally.	Campbell, D. T., & Stanley, J. C. (1963). Experimental and Quasi-Experimental Designs for Research. Rand McNally.
Inne nazwy	blocked pre-post design, RBPP design, block-randomized pretest-posttest design, randomized block pre-post control group design	controlled experiment, true experimental design, randomized controlled design, treatment-control design
Pokrewne	6	4
Podsumowanie	The blocked pretest-posttest experimental design combines blocking — grouping participants into homogeneous strata before randomization — with pre- and post-intervention measurement. Blocking controls for known sources of variability (e.g., baseline ability, gender, site), while the pretest-posttest structure quantifies change scores directly. Together, they reduce error variance and increase statistical power compared to a simple pretest-posttest design, making this approach well suited to educational, clinical, and behavioral intervention studies.	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.