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| 파일럿 A/B 테스트× | 실용적 A/B 테스트× | |
|---|---|---|
| 분야 | 실험설계 | 실험설계 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 2000s–2010s (formalized in digital experimentation literature) | 1967 (pragmatic framing); 2007–2012 (large-scale tech A/B testing practice) |
| 창시자≠ | Derived from pilot study methodology (Kraemer et al., 2006) applied to A/B testing practice | Pragmatic trial framing: Schwartz & Lellouch (1967); A/B testing in technology: Ron Kohavi and colleagues at Microsoft (~2007–2012) |
| 유형≠ | Experimental design — feasibility study | Randomized comparative experiment |
| 원전≠ | 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 ↗ | Schwartz, D., & Lellouch, J. (1967). Explanatory and pragmatic attitudes in therapeutical trials. Journal of Chronic Diseases, 20(8), 637–648. DOI ↗ |
| 별칭 | pilot split test, feasibility A/B test, preliminary A/B experiment, pilot randomized comparison | pragmatic split test, real-world A/B experiment, pragmatic online experiment, pragmatic controlled experiment |
| 관련≠ | 5 | 3 |
| 요약≠ | 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 pragmatic A/B test is a randomized comparative experiment that evaluates two alternatives — a control (A) and a treatment (B) — under real-world operating conditions rather than tightly controlled laboratory settings. Rooted in the pragmatic-versus-explanatory trial distinction introduced by Schwartz and Lellouch in 1967 and brought to large-scale practice by online experimentation teams at Microsoft, Google, and Amazon, it prioritizes decision-relevant effectiveness over internal mechanistic explanation. |
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