পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| ব্লকড এ/বি টেস্ট× | ফ্যাক্টোরিয়াল A/B টেস্ট× | |
|---|---|---|
| ক্ষেত্র | পরীক্ষামূলক নকশা | পরীক্ষামূলক নকশা |
| পরিবার | Process / pipeline | Process / pipeline |
| উদ্ভবের বছর≠ | 1926 (blocking principle); 2000s–2010s (online A/B testing application) | Factorial design: 1920s–1930s; applied online as factorial A/B test: 2000s–2010s |
| প্রবর্তক≠ | R. A. Fisher (blocking principle); adapted to online A/B testing by industry practitioners | Ronald A. Fisher (factorial design); digital A/B testing popularized by Google, Microsoft, and Amazon in the 2000s |
| ধরন≠ | Randomized controlled experiment with variance reduction | Controlled online/field experiment |
| মৌলিক উৎস≠ | Fisher, R. A. (1926). The arrangement of field experiments. Journal of the Ministry of Agriculture of Great Britain, 33, 503–513. link ↗ | Kohavi, R., Tang, D., & Xu, Y. (2020). Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing. Cambridge University Press. ISBN: 978-1108724265 |
| অপর নাম | block-randomized A/B test, stratified A/B test, blocked split test, block-design A/B experiment | factorial split test, multi-factor A/B test, factorial online experiment, factorial controlled experiment |
| সম্পর্কিত≠ | 4 | 6 |
| সারসংক্ষেপ≠ | 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. | A factorial A/B test is a controlled online experiment that simultaneously manipulates two or more independent factors, each at two or more levels, exposing different user groups to every combination of factor levels. Rooted in Fisher's factorial design and operationalised at scale by tech companies, it enables researchers to estimate both the independent main effect of each factor and the interaction effects between factors — all from a single experimental run. |
| ScholarGateডেটাসেট ↗ |
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