Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Teste A/B Bloqueado× | Teste A/B Fatorial× | |
|---|---|---|
| Área | Delineamento experimental | Delineamento experimental |
| Família | Process / pipeline | Process / pipeline |
| Ano de origem≠ | 1926 (blocking principle); 2000s–2010s (online A/B testing application) | Factorial design: 1920s–1930s; applied online as factorial A/B test: 2000s–2010s |
| Autor original≠ | R. A. Fisher (blocking principle); adapted to online A/B testing by industry practitioners | Ronald A. Fisher (factorial design); digital A/B testing popularized by Google, Microsoft, and Amazon in the 2000s |
| Tipo≠ | Randomized controlled experiment with variance reduction | Controlled online/field experiment |
| Fonte seminal≠ | Fisher, R. A. (1926). The arrangement of field experiments. Journal of the Ministry of Agriculture of Great Britain, 33, 503–513. link ↗ | Kohavi, R., Tang, D., & Xu, Y. (2020). Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing. Cambridge University Press. ISBN: 978-1108724265 |
| Outros nomes | block-randomized A/B test, stratified A/B test, blocked split test, block-design A/B experiment | factorial split test, multi-factor A/B test, factorial online experiment, factorial controlled experiment |
| Relacionados≠ | 4 | 6 |
| Resumo≠ | 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 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. |
| ScholarGateConjunto de dados ↗ |
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