Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Kijaruba kilichopangwa kwa Kundi A/B× | Jaribio la Shambani× | |
|---|---|---|
| Nyanja | Muundo wa Majaribio | Muundo wa Majaribio |
| Familia | Process / pipeline | Process / pipeline |
| Mwaka wa asili≠ | 2010s (digital platforms); cluster RCT roots date to the 1970s–1980s | 1920s–1930s (agriculture); 1990s–2000s (social sciences) |
| Mwanzilishi≠ | Developed from cluster randomized trial methodology; popularized in digital experimentation by researchers at Facebook, LinkedIn, and Microsoft Research (2010s) | Formalized by R. A. Fisher (1935); systematized in social sciences by Harrison & List (2004) |
| Aina | Experimental design | Experimental design |
| Chanzo asilia≠ | Ugander, J., Karrer, B., Backstrom, L., & Kleinberg, J. (2013). Graph cluster randomization: Network exposure to multiple universes. Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 329–337. DOI ↗ | Harrison, G. W., & List, J. A. (2004). Field experiments. Journal of Economic Literature, 42(4), 1009–1055. DOI ↗ |
| Majina mbadala | cluster A/B test, group-randomized A/B test, network A/B test, cluster-level split test | field trial, natural field experiment, randomized field experiment, field RCT |
| Zinazohusiana≠ | 6 | 5 |
| Muhtasari≠ | A cluster randomized A/B test is an experimental design in which intact groups (clusters) — such as cities, schools, social network communities, or app user segments — are randomly assigned as whole units to either the treatment (A) or control (B) condition, rather than randomizing individual users or subjects. This approach is used when treatment effects would spill over between individuals if individual-level randomization were applied, or when the intervention must be delivered at the group level. | A field experiment applies the logic of a randomized controlled trial in a naturally occurring, real-world environment rather than an artificial laboratory. Participants are randomly assigned to treatment and control conditions while going about everyday activities, allowing researchers to estimate causal effects with high internal validity while preserving a level of ecological realism that laboratory settings cannot offer. The design is especially prominent in economics, public health, political science, and development research. |
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