手法を比較

	単盲検実施変形実験	二重盲検分割要因実験
分野	実験計画法	実験計画法
系統	Process / pipeline	Process / pipeline
提唱年	1940s–1950s (fractional factorial foundations); blinding conventions formalised through 1960s–1980s	1960s onward (combination widely used in pharmaceutical and food science research)
提唱者	Fractional factorial theory: R. L. Plackett & J. P. Burman (1946); single-blinding practice codified in clinical trial methodology (20th century)	Fractional factorial: Box & Hunter (1961); double-blind convention: clinical trial methodology (mid-20th century)
種類	Controlled experimental design	Controlled experimental design with blinding and factor-space reduction
原典	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
別名	single-masked fractional factorial, single-blind FFD, partially blinded fractional factorial, single-blind 2^(k-p) design	double-blind FFE, blinded fractional factorial design, double-blind FFD, masked fractional factorial experiment
関連	5	3
概要	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 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.