gpd-compare-experiment

To quantify theory-data agreement, this Skill computes statistical metrics like chi-squared, p-values, and pulls. It systematically identifies and quantifies compatibility between theoretical predictions and experimental or observational data.

Uncertainty propagation is handled natively during theory-data comparisons. The Skill propagates measurement and prediction uncertainties to generate accurate statistical tests and standardized discrepancy analysis reports.

Unit conversion and convention checks are enforced automatically when loading predictions and datasets. This ensures accurate theory-data comparisons by standardizing units before calculating quantified agreement metrics.

Yes, you can load theoretical predictions from workspace artifacts and data sources. The Skill validates models against experiments by comparing these loaded predictions with observational datasets.

The best way to generate publication-ready summaries is using this Skill's standardized reporting. It produces quantified metrics and figures directly from your theory-data discrepancy analysis for papers or debugging.