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| Crossover-A/B-Test× | Faktorieller A/B-Test× | |
|---|---|---|
| Fachgebiet | Versuchsplanung | Versuchsplanung |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 1949 (crossover design); 2000s (online A/B application) | Factorial design: 1920s–1930s; applied online as factorial A/B test: 2000s–2010s |
| Urheber≠ | Crossover design: E. J. Williams (1949); A/B testing framework: Ronald Fisher (experimental roots); modern online application widely attributed to Google and Microsoft experimentation teams | Ronald A. Fisher (factorial design); digital A/B testing popularized by Google, Microsoft, and Amazon in the 2000s |
| Typ≠ | Within-subject controlled experiment | Controlled online/field experiment |
| Wegweisende Quelle≠ | Jones, B., & Kenward, M. G. (2014). Design and Analysis of Cross-Over Trials (3rd ed.). Chapman and Hall/CRC. ISBN: 9781439861424 | Kohavi, R., Tang, D., & Xu, Y. (2020). Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing. Cambridge University Press. ISBN: 978-1108724265 |
| Aliasnamen | within-subject A/B test, crossover split test, repeated-measures A/B test, AB crossover experiment | factorial split test, multi-factor A/B test, factorial online experiment, factorial controlled experiment |
| Verwandt | 6 | 6 |
| Zusammenfassung≠ | A crossover A/B test is an experimental design in which the same participants or units are exposed to both treatment A and treatment B in sequence, with each serving as their own control. By eliminating between-subject variability, the design achieves higher statistical power than a standard parallel A/B test at the same sample size, but it requires careful handling of carryover effects and time-period confounds. | 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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