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| Ujian A/B (Eksperimen Terkawal Dalam Talian)× | Reka Bentuk Eksperimen Faktorial Penuh× | |
|---|---|---|
| Bidang | Reka Bentuk Eksperimen | Reka Bentuk Eksperimen |
| Keluarga | Hypothesis test | Hypothesis test |
| Tahun asal≠ | 1935 | 1926 |
| Pengasas≠ | Ron Kohavi et al. (Microsoft); conceptual roots in R. A. Fisher's randomized experiments (1935) | R. A. Fisher |
| Jenis≠ | Parametric comparison (frequentist or Bayesian) | Parametric factorial experiment |
| Sumber perintis≠ | Kohavi, R., Tang, D., & Xu, Y. (2020). Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing. Cambridge University Press. ISBN: 9781108724265 | Box, G. E. P., Hunter, J. S., & Hunter, W. G. (2005). Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.). Wiley. ISBN: 978-0471718130 |
| Alias | split test, controlled experiment, two-variant test, A/B Testi (Online Kontrollü Deney) | factorial experiment, 2^k factorial, full factorial, Faktöriyel Deneme Deseni (Full Factorial, 2^k) |
| Berkaitan≠ | 4 | 5 |
| Ringkasan≠ | An A/B test is a randomized controlled experiment that simultaneously exposes two groups of users to a control variant (A) and a treatment variant (B) in order to determine whether a measured outcome differs significantly between them. The modern online controlled experiment framework was systematized by Ron Kohavi and colleagues at Microsoft in the early 2000s, building on R. A. Fisher's classical randomization principles from 1935. It is the dominant causal inference tool in web product development, digital marketing, and experimentation platforms. | A full factorial design is a parametric experimental method in which every combination of factor levels is tested simultaneously, enabling the estimation of all main effects and all interaction effects in a single study. Rooted in R. A. Fisher's foundational work on designed experiments (1926) and systematically developed by Box, Hunter, and Hunter (2005) and Montgomery (2017), the 2^k form tests k two-level factors across 2^k experimental runs and is the benchmark against which all other factorial designs are measured. |
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