Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Krīškrustojuma A/B tests× | Bloķēts A/B tests× | |
|---|---|---|
| Nozare | Eksperimentu plānošana | Eksperimentu plānošana |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 1949 (crossover design); 2000s (online A/B application) | 1926 (blocking principle); 2000s–2010s (online A/B testing application) |
| Autors≠ | 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 |
| Tips≠ | Within-subject controlled experiment | Randomized controlled experiment with variance reduction |
| Pirmavots≠ | 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 ↗ |
| Citi nosaukumi | 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 |
| Saistītās≠ | 6 | 4 |
| Kopsavilkums≠ | 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. |
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