Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Test A/B Factorial× | Experimento multibranzo× | |
|---|---|---|
| Campo | Diseño experimental | Diseño experimental |
| Familia | Process / pipeline | Process / pipeline |
| Año de origen≠ | Factorial design: 1920s–1930s; applied online as factorial A/B test: 2000s–2010s | 1990s–2000s (clinical formalization); multi-arm concept implicit in ANOVA-era factorial designs |
| Autor original≠ | Ronald A. Fisher (factorial design); digital A/B testing popularized by Google, Microsoft, and Amazon in the 2000s | Developed within clinical trials methodology; formalized by Parmar, Royston and colleagues (UK MRC CTU, early 2000s) |
| Tipo≠ | Controlled online/field experiment | Experimental design |
| Fuente seminal≠ | Kohavi, R., Tang, D., & Xu, Y. (2020). Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing. Cambridge University Press. ISBN: 978-1108724265 | 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 | factorial split test, multi-factor A/B test, factorial online experiment, factorial controlled experiment | multi-arm trial, multiple-arm experiment, multi-group experiment, many-arm design |
| Relacionados≠ | 6 | 5 |
| Resumen≠ | A factorial A/B test is a controlled online experiment that simultaneously manipulates two or more independent factors, each at two or more levels, exposing different user groups to every combination of factor levels. Rooted in Fisher's factorial design and operationalised at scale by tech companies, it enables researchers to estimate both the independent main effect of each factor and the interaction effects between factors — all from a single experimental run. | 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. |
| ScholarGateConjunto de datos ↗ |
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