विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| डबल-ब्लाइंड फ़्रैक्शनल फ़ैक्टोरियल प्रयोग× | आंशिक गुणनखंड प्रयोग× | |
|---|---|---|
| क्षेत्र | प्रयोगात्मक अभिकल्प | प्रयोगात्मक अभिकल्प |
| परिवार | Process / pipeline | Process / pipeline |
| उद्भव वर्ष≠ | 1960s onward (combination widely used in pharmaceutical and food science research) | 1945 (Finney); broader development 1950s–1970s by Box, Hunter |
| प्रवर्तक≠ | Fractional factorial: Box & Hunter (1961); double-blind convention: clinical trial methodology (mid-20th century) | D. J. Finney (formal development); foundations in Ronald Fisher's factorial design work |
| प्रकार≠ | Controlled experimental design with blinding and factor-space reduction | 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 | double-blind FFE, blinded fractional factorial design, double-blind FFD, masked fractional factorial experiment | fractional factorial design, FFD, 2^(k-p) design, fractional replication |
| संबंधित≠ | 3 | 4 |
| सारांश≠ | A double-blind fractional factorial experiment combines two powerful methodological protections: fractional factorial design, which tests a carefully chosen subset of all possible factor combinations to achieve efficiency, and double-blind administration, which prevents both participants and assessors from knowing which treatment combination has been applied. The result is an experiment that is both resource-efficient and protected against expectation and assessment 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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