Full Factorial Design
Updated 2026-08-10
INTRODUCTION
English translation pending.
CORE DEFINITION
An experimental design from statistics, in which every level of every factor is crossed with every level of the others. The core proposition is that exhaustive crossing allows unbiased estimation of main effects and of all interaction effects, which fractional designs may confound. The key qualification is cost: the number of runs grows exponentially with the number of factors, so full factorials become impractical beyond a few factors.
SCAFFOLDING EFFECT
Reduce cognitive load
- Factor listing: write out every factor and its levels before designing runs. - Interaction check: verify whether factors might interact before choosing a design. - Cost trade: switch to a fractional design when the full grid becomes infeasible.
Anchor fast decisions
Crossing all levels means every factor is varied in combination with every level of the others, so the effect of one factor can be separated from the rest. This structure also makes interaction effects estimable, because the design contains the cells where joint effects would appear. The cost is combinatorial growth, which forces a trade between completeness and feasibility.
MINIMUM ACTION
In progress 0/1Practice this model in one real situation:
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Source support: Explicit
- en.wikipedia.orghttps://en.wikipedia.org/wiki/Factorial_experimentverified
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