השוואת שיטות
סקרו את השיטות שבחרתם זו לצד זו; שורות שבהן יש הבדל מודגשות.
| מבחן A/B אקראי-מצברים× | מבחן A/B חסום× | |
|---|---|---|
| תחום | תכנון ניסויים | תכנון ניסויים |
| משפחה | Process / pipeline | Process / pipeline |
| שנת המקור≠ | 2010s (digital platforms); cluster RCT roots date to the 1970s–1980s | 1926 (blocking principle); 2000s–2010s (online A/B testing application) |
| הוגה השיטה≠ | Developed from cluster randomized trial methodology; popularized in digital experimentation by researchers at Facebook, LinkedIn, and Microsoft Research (2010s) | R. A. Fisher (blocking principle); adapted to online A/B testing by industry practitioners |
| סוג≠ | Experimental design | Randomized controlled experiment with variance reduction |
| מקור מכונן≠ | Ugander, J., Karrer, B., Backstrom, L., & Kleinberg, J. (2013). Graph cluster randomization: Network exposure to multiple universes. Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 329–337. DOI ↗ | Fisher, R. A. (1926). The arrangement of field experiments. Journal of the Ministry of Agriculture of Great Britain, 33, 503–513. link ↗ |
| כינויים | cluster A/B test, group-randomized A/B test, network A/B test, cluster-level split test | block-randomized A/B test, stratified A/B test, blocked split test, block-design A/B experiment |
| קשורות≠ | 6 | 4 |
| תקציר≠ | A cluster randomized A/B test is an experimental design in which intact groups (clusters) — such as cities, schools, social network communities, or app user segments — are randomly assigned as whole units to either the treatment (A) or control (B) condition, rather than randomizing individual users or subjects. This approach is used when treatment effects would spill over between individuals if individual-level randomization were applied, or when the intervention must be delivered at the group level. | 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. |
| ScholarGateמערך נתונים ↗ |
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