เปรียบเทียบวิธี
ดูวิธีที่เลือกเทียบกันแบบเคียงข้าง แถวที่ต่างกันจะถูกเน้นไว้
| การทดสอบแบบครอสโอเวอร์ A/B× | Blocked A/B Test× | |
|---|---|---|
| สาขาวิชา | การออกแบบการทดลอง | การออกแบบการทดลอง |
| ตระกูล | Process / pipeline | Process / pipeline |
| ปีกำเนิด≠ | 1949 (crossover design); 2000s (online A/B application) | 1926 (blocking principle); 2000s–2010s (online A/B testing application) |
| ผู้ริเริ่ม≠ | 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 | R. A. Fisher (blocking principle); adapted to online A/B testing by industry practitioners |
| ประเภท≠ | Within-subject controlled experiment | Randomized controlled experiment with variance reduction |
| แหล่งต้นตำรับ≠ | Jones, B., & Kenward, M. G. (2014). Design and Analysis of Cross-Over Trials (3rd ed.). Chapman and Hall/CRC. ISBN: 9781439861424 | Fisher, R. A. (1926). The arrangement of field experiments. Journal of the Ministry of Agriculture of Great Britain, 33, 503–513. link ↗ |
| ชื่อเรียกอื่น | within-subject A/B test, crossover split test, repeated-measures A/B test, AB crossover experiment | block-randomized A/B test, stratified A/B test, blocked split test, block-design A/B experiment |
| ที่เกี่ยวข้อง≠ | 6 | 4 |
| สรุป≠ | 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 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. |
| ScholarGateชุดข้อมูล ↗ |
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