yogsoth-ai Strategy: Comparison Design
Question: How much better is our method than the baseline?
Methodology
- Fair Comparison Protocol (Bouthillier 2021): Control all confounds, same compute budget, same tuning effort.
- Multi-Baseline Comparison: Compare against multiple baselines (SOTA, simple, ablated).
- Multi-Dataset Evaluation: Test across diverse datasets to avoid dataset-specific overfitting.
- Bayesian Comparison (Benavoli 2017): Posterior probability of superiority, not just p-values.
- Bootstrap/Permutation Tests: Non-parametric significance without distributional assumptions.
Execution Flow
- baseline-selection → Select appropriate baselines (SOTA, simple, oracle)
- metric-specification → Define primary metric and secondary metrics
- sample-size-estimation → Power analysis for detecting meaningful differences
- seed-protocol-design → Ensure fair random initialization across methods
- environment-specification → Lock environment to prevent confounds
- reproducibility-protocol (tactic) → Ensure all results are reproducible
- statistical-method-selection (tactic) → Choose Bayesian or frequentist comparison
Budget Gate
| Comparison Scope | Baselines | Datasets | Seeds | Min Runs |
|---|---|---|---|---|
| Minimal | 1 SOTA + 1 simple | 1 | 3 | 6 |
| Standard | 2-3 baselines | 2-3 | 5 | 30-45 |
| Comprehensive | 4+ baselines | 3-5 | 5-10 | 100+ |
| Publication-ready | All relevant | 5+ | 10+ | 200+ |
Available Tactics
Optional, no fixed order; the final leaf is always a sop.
| Tactic | When to use |
|---|---|
| reproducibility-protocol | Ensure experiment reproducibility through systematic environment and seed control |
| statistical-method-selection | Select appropriate statistical methods for experiment analysis |
Available SOPs
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use |
|---|---|
| baseline-selection | Select appropriate baselines for experimental comparison |
| environment-specification | SOP: define complete experiment environment specification |
| metric-specification | Define experiment metrics and significance standards |
| sample-size-estimation | SOP: power analysis and required experiment count estimation |
| seed-protocol-design | SOP: design random seed strategy for reproducibility |