experiment-rigor

To design research experiments with decision-grade evidence, structure hypotheses around a specific adoption or rejection decision, define method-valid setups, set confirm/reject/inconclusive thresholds, and store raw metrics under kb/research/data/ for traceability.

Method-validity gating in hypothesis validation forces explicit checks that an experiment exposes the method's real intended capability. It is needed to ensure a negative result reflects a true failure rather than an invalid experimental setup.

Controlled baseline comparisons for evaluation metrics require naming specific comparator baselines, controlling variables, defining primary metrics alongside guardrails, and establishing a trial plan with explicit stopping rules and budget constraints.

Experiment validation produces inconclusive results due to invalid tests, implementation failures, or insufficient signal. Classify these negative outcomes distinctly from true negatives and trade-off failures before concluding a hypothesis is rejected.

The best way to structure H to E to F workflows for research rigor is keeping the narrative across hypotheses, experiments, and findings while enforcing explicit decision questions, method-validity gates, and structured metric storage to prevent ambiguous conclusions.

Yes, you can use experiment-rigor to review existing hypotheses by validating the decision question, checking baseline fairness, verifying method-validity gates, and confirming that stopping rules and confirm/reject criteria are decisive and properly defined.