In the statistical analysis of the results from factorial experiments, the sparsity-of-effects principle states that a system is usually dominated by main effects and low-order interactions. Thus it is most likely that main (single factor) effects and two-factor interactions are the most significant responses in a factorial experiment. In other words, higher order interactions such as three-factor interactions are very rare. This is sometimes referred to as the hierarchical ordering principle.[1] The sparsity-of-effects principle actually refers to the idea that only a few effects in a factorial experiment will be statistically significant.[1]
This principle is only valid on the assumption of a factor space far from a stationary point.[2]
See also
References
- ^ a b Wu, C. F. Jeff; Hamada, Michael (2000). Experiments: Planning, analysis, and parameter design optimization. New York: Wiley. p. 112. ISBN 0-471-25511-4.
- ^ Box, G.E.P.; Hunter, J.S.; Hunter, W.G. (2005). Statistics for Experimenters: Design, Innovation, and Discovery. Wiley. p. 208. ISBN 0471718130.