Confronta i metodi
Esamina i metodi selezionati fianco a fianco; le righe che differiscono sono evidenziate.
| Test A/B Pilota× | Test fattoriale A/B× | |
|---|---|---|
| Campo | Disegno sperimentale | Disegno sperimentale |
| Famiglia | Process / pipeline | Process / pipeline |
| Anno di origine≠ | 2000s–2010s (formalized in digital experimentation literature) | Factorial design: 1920s–1930s; applied online as factorial A/B test: 2000s–2010s |
| Ideatore≠ | Derived from pilot study methodology (Kraemer et al., 2006) applied to A/B testing practice | Ronald A. Fisher (factorial design); digital A/B testing popularized by Google, Microsoft, and Amazon in the 2000s |
| Tipo≠ | Experimental design — feasibility study | Controlled online/field experiment |
| Fonte seminale≠ | 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 ↗ | Kohavi, R., Tang, D., & Xu, Y. (2020). Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing. Cambridge University Press. ISBN: 978-1108724265 |
| Alias | pilot split test, feasibility A/B test, preliminary A/B experiment, pilot randomized comparison | factorial split test, multi-factor A/B test, factorial online experiment, factorial controlled experiment |
| Correlati≠ | 5 | 6 |
| Sintesi≠ | 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 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. |
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