方法对比
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| 单盲A/B测试× | 双盲 A/B 测试× | |
|---|---|---|
| 领域 | 实验设计 | 实验设计 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | mid-20th century (blinded RCT framework); A/B test nomenclature ~1990s–2000s | 1935 (Fisher's formal randomized design); double-blinding in A/B testing: 1990s–2000s |
| 提出者≠ | Fisher, R. A. (randomisation basis); blinding practice formalised in clinical trials mid-20th century | Evolved from clinical trial methodology; early systematic blinding attributed to James Lind (1753) and formalized by R. A. Fisher (1935) |
| 类型≠ | Controlled experiment with partial blinding | Randomized controlled experiment with blinding |
| 开创性文献≠ | Kohavi, R., Tang, D., & Xu, Y. (2020). Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing. Cambridge University Press. ISBN: 978-1108724265 | Schulz, K. F., Altman, D. G., & Moher, D. (2010). CONSORT 2010 Statement: Updated guidelines for reporting parallel group randomised trials. BMJ, 340, c332. DOI ↗ |
| 别名 | single-masked A/B test, single-blind split test, blinded two-condition experiment, participant-blind A/B test | double-blind split test, double-blinded A/B experiment, blinded two-arm randomized experiment, double-blind controlled A/B trial |
| 相关≠ | 6 | 5 |
| 摘要≠ | 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 double-blind A/B test is a randomized experiment that compares two variants — a control (A) and a treatment (B) — while concealing group assignment from both participants and those administering or assessing the experiment. Combining the causal isolation of randomized assignment with blinding on both sides eliminates expectation-driven bias from participants and evaluator bias from analysts or administrators, producing cleaner causal estimates of treatment effect. |
| ScholarGate数据集 ↗ |
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