experimental-design

name	experimental-design
description	Best practices for designing reproducible ML experiments. Use when planning ablations, baselines, or controlled experiments.
metadata	{"category":"experiment","trigger-keywords":"experiment,ablation,baseline,control,hypothesis,reproducib","applicable-stages":"9,10,12","priority":"2","version":"1.0","author":"researchclaw","references":"Bouthillier et al., Accounting for Variance in ML Benchmarks, MLSys 2021"}

Experimental Design Best Practice

1. ALWAYS include meaningful baselines (not just random):
   • At least one classical method baseline
   • At least one recent SOTA method baseline
   • A simple-but-strong baseline (e.g., linear probe, k-NN)
2. Use MULTIPLE random seeds (minimum 3, ideally 5)
3. Report mean +/- std across seeds
4. Design ablations that isolate EACH key component:
   • Remove one component at a time
   • Each ablation must be meaningfully different from baseline
5. Control variables: change only ONE thing per comparison
6. Use standard splits (train/val/test) — never test on training data
7. Report wall-clock time and memory usage alongside accuracy