विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| एकल-अंध आंशिक गुणनखंडीय प्रयोग× | आंशिक गुणनखंड प्रयोग× | |
|---|---|---|
| क्षेत्र | प्रयोगात्मक अभिकल्प | प्रयोगात्मक अभिकल्प |
| परिवार | Process / pipeline | Process / pipeline |
| उद्भव वर्ष≠ | 1940s–1950s (fractional factorial foundations); blinding conventions formalised through 1960s–1980s | 1945 (Finney); broader development 1950s–1970s by Box, Hunter |
| प्रवर्तक≠ | Fractional factorial theory: R. L. Plackett & J. P. Burman (1946); single-blinding practice codified in clinical trial methodology (20th century) | D. J. Finney (formal development); foundations in Ronald Fisher's factorial design work |
| प्रकार≠ | Controlled experimental design | Quantitative experimental design |
| मौलिक स्रोत | Box, G. E. P., Hunter, J. S., & Hunter, W. G. (2005). Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.). Wiley-Interscience. ISBN: 978-0471718130 | Box, G. E. P., Hunter, J. S., & Hunter, W. G. (2005). Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.). Wiley-Interscience. ISBN: 978-0471718130 |
| उपनाम | single-masked fractional factorial, single-blind FFD, partially blinded fractional factorial, single-blind 2^(k-p) design | fractional factorial design, FFD, 2^(k-p) design, fractional replication |
| संबंधित≠ | 5 | 4 |
| सारांश≠ | A single-blind fractional factorial experiment studies multiple factors simultaneously by testing only a strategically chosen subset — a fraction — of all possible factor-level combinations, while keeping participants unaware of which treatment condition they receive. This design yields substantial information about main effects and selected interactions at a fraction of the cost of a full factorial experiment, with single-blinding reducing participant-side response bias. | A fractional factorial experiment is a resource-efficient experimental design that tests only a carefully chosen fraction of all possible factor-level combinations. By exploiting the principle that high-order interactions are usually negligible, it identifies the main effects and low-order interactions of k factors using far fewer runs than a full factorial design — making it the workhorse of industrial and engineering screening experiments. |
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