Sparsity-of-effects principle
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
- Occam's Razor
- Pareto principle
References
- ↑ 1.0 1.1 Wu, C. F. Jeff; Hamada, Michael (2000). Experiments: Planning, analysis, and parameter design optimization. New York: Wiley. pp. 112. ISBN 0-471-25511-4.
- ↑ Statistics for Experimenters: Design, Innovation, and Discovery. Wiley. 2005. p. 208. ISBN 0471718130.
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