Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Односліпий A/B-тест× | Блокований A/B тест× | |
|---|---|---|
| Галузь | Планування експерименту | Планування експерименту |
| Родина | Process / pipeline | Process / pipeline |
| Рік появи≠ | mid-20th century (blinded RCT framework); A/B test nomenclature ~1990s–2000s | 1926 (blocking principle); 2000s–2010s (online A/B testing application) |
| Автор методу≠ | Fisher, R. A. (randomisation basis); blinding practice formalised in clinical trials mid-20th century | R. A. Fisher (blocking principle); adapted to online A/B testing by industry practitioners |
| Тип≠ | Controlled experiment with partial blinding | Randomized controlled experiment with variance reduction |
| Основоположне джерело≠ | Kohavi, R., Tang, D., & Xu, Y. (2020). Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing. Cambridge University Press. ISBN: 978-1108724265 | Fisher, R. A. (1926). The arrangement of field experiments. Journal of the Ministry of Agriculture of Great Britain, 33, 503–513. link ↗ |
| Інші назви | single-masked A/B test, single-blind split test, blinded two-condition experiment, participant-blind A/B test | block-randomized A/B test, stratified A/B test, blocked split test, block-design A/B experiment |
| Пов'язані≠ | 6 | 4 |
| Підсумок≠ | A single-blind A/B test is a controlled two-condition experiment in which participants are randomised to condition A (control) or condition B (treatment) but are kept unaware of which condition they have received, while researchers and analysts remain aware. The blind prevents participants from changing their behaviour in response to knowledge of their assignment, reducing demand characteristics and response bias while still allowing the investigator to monitor the trial. | 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. |
| ScholarGateНабір даних ↗ |
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