Orthogonal Design
Updated 2026-08-13
INTRODUCTION
English translation pending.
CORE DEFINITION
Orthogonal design has two related senses: as an analytical property, orthogonality means factors vary independently so changing one does not affect the others; as an experimental method, an orthogonal array tests many factors with a minimum number of runs while keeping their effects separable. The core proposition is that independence reduces complexity, because orthogonal components can be varied, tested, and optimized separately. The key qualification is that independence is a design property, not a natural given: factors that interact cannot be treated as separable without confounding their effects.
SCAFFOLDING EFFECT
Reduce cognitive load
- Factor listing: enumerate the factors and the levels each one will take. - Array selection: choose an orthogonal array that covers the combinations with the fewest runs. - Confounding check: verify that factors you need to separate are not entangled in the chosen design.
Anchor fast decisions
An orthogonal array balances the levels so that every level of every factor appears equally often with every level of the others. That balance lets the main effect of each factor be estimated without interference from the rest. Because the runs are chosen rather than exhaustive, the same information is obtained from far fewer experiments than a full factorial would require.
MINIMUM ACTION
In progress 0/1Practice this model in one real situation:
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- en.wikipedia.orghttps://en.wikipedia.org/wiki/Orthogonal_arrayverified
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