| name | experiment-design |
| description | Transform validated hypotheses into rigorous, executable experiment designs |
| version | 1.0.0 |
| category | experiment-execution |
| type | campaign |
| strategies | ["factor-level-design","ablation-design","comparison-design","scaling-design","robustness-design"] |
| tactics | ["statistical-method-selection","reproducibility-protocol","budget-constrained-design"] |
| dependencies | {"strategies":["ablation-design","comparison-design","experiment-execution-factor-level-design","robustness-design","scaling-design"],"tactics":["budget-constrained-design","reproducibility-protocol","statistical-method-selection"],"sops":["context-checkpoint","context-init","design-synthesis","experiment-execution-paper-overview","experiment-execution-paper-research","experiment-execution-paper-search","experiment-execution-quality-gate-check","experiment-execution-saturation-detection","experiment-execution-web-research","experiment-execution-web-search"]} |
Campaign: Experiment Design
Positioning: What experiment to run — transform a validated hypothesis into a rigorous experiment design that maximizes information yield per compute dollar.
HARD-GATE
Before entering this campaign, the following must be satisfied:
| Gate | Requirement |
|---|
| Hypothesis | A falsifiable hypothesis with clearly stated IV/DV exists |
| Scope | Research question is bounded (not open-ended exploration) |
| Resources | Preliminary compute/time budget is stated |
| Prior Work | Relevant baselines and datasets have been identified |
If any gate fails, route back to hypothesis-generation or research-question refinement.
Campaign Goal
Produce a complete experiment design document that specifies:
- What factors to vary and at what levels
- What to measure and how to determine significance
- What baselines to compare against
- How to ensure reproducibility
- An executable configuration ready for implementation
Strategy Selection
| Signal in Hypothesis | Strategy | When to Use |
|---|
| "Factor X affects Y" | factor-level-design | Testing effects of specific variables |
| "Component C contributes to performance" | ablation-design | Understanding component contributions |
| "Method M outperforms baseline B" | comparison-design | Claiming superiority over existing work |
| "Performance scales with resource R" | scaling-design | Understanding scaling behavior |
| "Method works under condition C" | robustness-design | Testing failure boundaries |
Multiple strategies may be composed for complex hypotheses.
Budget Gate
| Tier | GPU-hours | Max Factors | Max Runs | Strategy Constraint |
|---|
| Micro | < 10 | 3 | 20 | Fractional factorial or single ablation |
|
