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| Ujian A/B Terhalang× | Eksperimen pelbagai lengan× | |
|---|---|---|
| Bidang | Reka Bentuk Eksperimen | Reka Bentuk Eksperimen |
| Keluarga | Process / pipeline | Process / pipeline |
| Tahun asal≠ | 1926 (blocking principle); 2000s–2010s (online A/B testing application) | 1990s–2000s (clinical formalization); multi-arm concept implicit in ANOVA-era factorial designs |
| Pengasas≠ | R. A. Fisher (blocking principle); adapted to online A/B testing by industry practitioners | Developed within clinical trials methodology; formalized by Parmar, Royston and colleagues (UK MRC CTU, early 2000s) |
| Jenis≠ | Randomized controlled experiment with variance reduction | Experimental design |
| Sumber perintis≠ | Fisher, R. A. (1926). The arrangement of field experiments. Journal of the Ministry of Agriculture of Great Britain, 33, 503–513. link ↗ | 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 | block-randomized A/B test, stratified A/B test, blocked split test, block-design A/B experiment | multi-arm trial, multiple-arm experiment, multi-group experiment, many-arm design |
| Berkaitan≠ | 4 | 5 |
| Ringkasan≠ | A blocked A/B test is an experimental design that partitions units (users, subjects, or clusters) into homogeneous blocks before randomly assigning them to treatment A or treatment B within each block. Blocking reduces within-experiment noise by ensuring that known sources of variation — such as device type, geography, or user tenure — are balanced across conditions, yielding more precise estimates of the treatment effect than a simple unblocked A/B test. | 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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