Linganisha mbinu

	Muundo wa Kundi la Kidhibiti cha Majaribio	Muundo wa Kimsingi wa Kundi Dhibiti la Majaribio
Nyanja	Muundo wa Majaribio	Muundo wa Majaribio
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	Mid-20th century; widely formalized by 1980s–2000s	1935 (Fisher); 1963 (Campbell & Stanley codification)
Mwanzilishi	Established through clinical and behavioral research traditions; formalized by Bradford Hill and colleagues in mid-20th century trial methodology	Ronald A. Fisher; systematised by Donald T. Campbell & Julian C. Stanley
Aina	Experimental design (pilot/feasibility variant)	Experimental research design
Chanzo asilia	Thabane, L., Ma, J., Chu, R., Cheng, J., Ismaila, A., Rios, L. P., Robson, R., Thabane, M., Giangregorio, L., & Goldsmith, C. H. (2010). A tutorial on pilot studies: the what, why and how. BMC Medical Research Methodology, 10, 1.	Campbell, D. T., & Stanley, J. C. (1963). Experimental and Quasi-Experimental Designs for Research. Rand McNally.
Majina mbadala	pilot controlled experiment, pilot RCT feasibility study, small-scale controlled trial, pilot control group study	controlled experiment, true experimental design, randomized controlled design, treatment-control design
Zinazohusiana	4	4
Muhtasari	A pilot control group experimental design is a small-scale, preliminary experiment that includes both a treatment group and a control group, conducted before the main study to test whether the full trial is feasible. It produces early effect-size estimates, identifies protocol problems, and confirms that random (or systematic) assignment to conditions is workable — all while generating a genuine comparison between treated and untreated participants.	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.