Design of Experiments (DOE) is a systematic statistical methodology for planning, conducting, and analyzing controlled experiments to understand and optimize complex processes and systems. Originally developed by Ronald A. Fisher in the 1920s for agricultural research, DOE has evolved into an indispensable tool across manufacturing, pharmaceuticals, materials science, biotechnology, and virtually every field requiring empirical optimization. DOE enables experimenters to efficiently extract maximum information from minimum experimental runs by strategically manipulating multiple factors simultaneously, rather than the wasteful one-factor-at-a-time approach. The fundamental insight underlying DOE is the principle of effect sparsity—relatively few factors and interactions dominate system behavior, allowing fractional designs to capture essential relationships while dramatically reducing experimental cost and time.