Research Design, Methodology, and Rigor Questions

Approach summary
Start by assuming tiny true effects and high overfitting risk. Prioritize strong, reproducible evidence: conservative baselines, rigorous validation splits, and targeted ablations that isolate mechanisms.

Strategy

• Strong baselines first
  • Reproduce simple but competitive baselines (e.g., tuned logistic/regressor, random features, data augmentation only). A new method must beat these reliably.
• Validation design
  • Use nested cross-validation or repeated random splits to estimate variance. Hold out a final locked test set for the claim.
• Ablation plan
  • Pre-register a small set (~3–6) of hypothesis-driven ablations: remove key components individually, replace with controlled alternatives, and run pairwise comparisons.
• Statistical testing
  • Apply paired tests (e.g., Wilcoxon signed-rank or paired t if normality holds) across folds; adjust for multiple comparisons (Benjamini–Hochberg) when testing multiple ablations.
• Power and sample-effort trade-off
  • Run a pilot to estimate effect size and variance; compute required runs for desired power (e.g., 80%). If required runs are prohibitive, favor stronger baselines and tighter claims.

Trade-offs

• More tests → higher chance of false positives and greater computational cost; need corrections and pre-registration.
• Stronger baselines → higher bar but reduces risk of publishing artifacts.
• More runs/permutations → better confidence but linear increase in cost; use stratified sampling and variance-reduction (control seeds, common random numbers).

Template for robust claims

• Claim statement (precise metric, dataset, and seed regime)
• Experimental protocol (data splits, hyperparameter tuning budget, number of repeats)
• Baselines (list + how tuned)
• Ablations (pre-registered list and rationale)
• Statistical results (mean ± SE, p-values, effect size, correction method)
• Power analysis and limitations
• Reproducibility artifacts (code, seeds, config)

This balances scientific rigor with computational practicality and is appropriate for research-grade claims.