Research Note I

Evaluating Methodological Rigor in Applied Machine Learning Research

Methodological rigor in applied machine learning research depends on the alignment between research claims and experimental design. Evaluation begins with assessing whether the methodology meaningfully tests the stated hypothesis, including the appropriateness of baselines, dataset selection, and model comparisons.

Special attention should be given to dataset construction, train–test separation, and evaluation protocols, as subtle methodological flaws can materially inflate reported performance. Transparent reporting of assumptions, limitations, and failure cases strengthens the credibility of research findings.

Reviewer note: Claims are only as strong as the experimental controls that support them.