Bandingkan metode
Tinjau metode pilihan Anda berdampingan; baris yang berbeda akan disorot.
| Uji Coba A/B Percontohan× | Eksperimen multi-lengan× | |
|---|---|---|
| Bidang | Desain Eksperimen | Desain Eksperimen |
| Keluarga | Process / pipeline | Process / pipeline |
| Tahun asal≠ | 2000s–2010s (formalized in digital experimentation literature) | 1990s–2000s (clinical formalization); multi-arm concept implicit in ANOVA-era factorial designs |
| Pencetus≠ | Derived from pilot study methodology (Kraemer et al., 2006) applied to A/B testing practice | Developed within clinical trials methodology; formalized by Parmar, Royston and colleagues (UK MRC CTU, early 2000s) |
| Tipe≠ | Experimental design — feasibility study | Experimental design |
| Sumber perintis≠ | 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), 1. DOI ↗ | Royston, P., Parmar, M. K. B., & Qian, W. (2003). Novel designs for multi-arm clinical trials with survival outcomes with an application in ovarian cancer. Statistics in Medicine, 22(14), 2239–2256. DOI ↗ |
| Alias | pilot split test, feasibility A/B test, preliminary A/B experiment, pilot randomized comparison | multi-arm trial, multiple-arm experiment, multi-group experiment, many-arm design |
| Terkait | 5 | 5 |
| Ringkasan≠ | A Pilot A/B test is a small-scale, preliminary split-test experiment run before a full A/B test to assess feasibility, estimate effect sizes, detect operational problems, and validate measurement instruments. Participants are randomly assigned to a control condition (A) and a treatment condition (B), but the study is explicitly underpowered — its purpose is to inform the design of the definitive test, not to yield a conclusive comparison. | A multi-arm experiment simultaneously compares three or more treatment or intervention conditions — each called an arm — against a shared control or against one another. By testing multiple alternatives in a single study, it yields more information per participant than running separate two-group experiments sequentially, while controlling the overall Type I error rate through pre-specified comparison strategies. |
| ScholarGateSet data ↗ |
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