เปรียบเทียบวิธี
ดูวิธีที่เลือกเทียบกันแบบเคียงข้าง แถวที่ต่างกันจะถูกเน้นไว้
| การทดสอบ A/B นำร่อง× | การทดสอบ A/B แบบแฟกทอเรียล× | |
|---|---|---|
| สาขาวิชา | การออกแบบการทดลอง | การออกแบบการทดลอง |
| ตระกูล | Process / pipeline | Process / pipeline |
| ปีกำเนิด≠ | 2000s–2010s (formalized in digital experimentation literature) | Factorial design: 1920s–1930s; applied online as factorial A/B test: 2000s–2010s |
| ผู้ริเริ่ม≠ | 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 |
| ประเภท≠ | Experimental design — feasibility study | Controlled online/field experiment |
| แหล่งต้นตำรับ≠ | 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 |
| ชื่อเรียกอื่น | 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 |
| ที่เกี่ยวข้อง≠ | 5 | 6 |
| สรุป≠ | 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. |
| ScholarGateชุดข้อมูล ↗ |
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