Sammenlign metoder
Gjennomgå de valgte metodene side om side; rader som avviker, er uthevet.
| Enkelt-blind A/B-test× | Blokkert A/B-test× | |
|---|---|---|
| Fagfelt | Forsøksdesign | Forsøksdesign |
| Familie | Process / pipeline | Process / pipeline |
| Opprinnelsesår≠ | mid-20th century (blinded RCT framework); A/B test nomenclature ~1990s–2000s | 1926 (blocking principle); 2000s–2010s (online A/B testing application) |
| Opphavsperson≠ | 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 |
| Type≠ | Controlled experiment with partial blinding | Randomized controlled experiment with variance reduction |
| Opprinnelig kilde≠ | 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 ↗ |
| Alias | 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 |
| Relaterte≠ | 6 | 4 |
| Sammendrag≠ | 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. |
| ScholarGateDatasett ↗ |
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