Confronta i metodi
Esamina i metodi selezionati fianco a fianco; le righe che differiscono sono evidenziate.
| Test fattoriale A/B× | Esperimento Fattoriale Frazionato× | |
|---|---|---|
| Campo | Disegno sperimentale | Disegno sperimentale |
| Famiglia | Process / pipeline | Process / pipeline |
| Anno di origine≠ | Factorial design: 1920s–1930s; applied online as factorial A/B test: 2000s–2010s | 1945 (Finney); broader development 1950s–1970s by Box, Hunter |
| Ideatore≠ | Ronald A. Fisher (factorial design); digital A/B testing popularized by Google, Microsoft, and Amazon in the 2000s | D. J. Finney (formal development); foundations in Ronald Fisher's factorial design work |
| Tipo≠ | Controlled online/field experiment | Quantitative experimental design |
| Fonte seminale≠ | Kohavi, R., Tang, D., & Xu, Y. (2020). Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing. Cambridge University Press. ISBN: 978-1108724265 | Box, G. E. P., Hunter, J. S., & Hunter, W. G. (2005). Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.). Wiley-Interscience. ISBN: 978-0471718130 |
| Alias | factorial split test, multi-factor A/B test, factorial online experiment, factorial controlled experiment | fractional factorial design, FFD, 2^(k-p) design, fractional replication |
| Correlati≠ | 6 | 4 |
| Sintesi≠ | 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 fractional factorial experiment is a resource-efficient experimental design that tests only a carefully chosen fraction of all possible factor-level combinations. By exploiting the principle that high-order interactions are usually negligible, it identifies the main effects and low-order interactions of k factors using far fewer runs than a full factorial design — making it the workhorse of industrial and engineering screening experiments. |
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