diagnose

To interpret unexpected experimental results, analyze empirical result files and associated logs to identify error distribution patterns, generate root-cause hypotheses across system layers, and assess validity to pinpoint specific faults.

The best way to analyze error distributions in experiment data is to characterize the errors to identify systematic patterns, evaluate potential root causes across Model, Workflow, Interface, Methodology, and Human layers, and recommend concrete next steps.

Assess the validity of empirical findings by checking construct, statistical, external, and ground truth validity. This validates your A/B test outcomes and helps explain significant performance drops or underperforming features.

Diagnosing experiment results requires Python to process empirical data. You need to provide result files, such as experiment run CSVs, and associated logs for the analysis to generate actionable insights.

An A/B test showing a significant drop in conversion rates may stem from issues in specific system layers like Methodology or Interface. Analyze the result files to generate root-cause hypotheses and identify the exact implementation fault.