Audit scientific analysis code for statistical correctness and data integrity.
npx skills add https://github.com/smestern/sciagent --skill rigor-reviewer
Please help me install this Agent Skill. Skill: rigor-reviewer Source: https://github.com/smestern/sciagent/tree/main/templates/skills/rigor-reviewer Command: npx skills add https://github.com/smestern/sciagent --skill rigor-reviewer
The rigor-reviewer skill helps identify scientific rigor violations in your analysis outputs, code, and claims.
Run the rigor-reviewer skill to audit your analysis and code for scientific rigor.
High-intent search queries and answers about installing and using this skill.
To audit data analysis for statistical correctness, you can use an analysis audit tool to check your code and outputs for inappropriate tests, assumption violations, and multiple-comparison errors. This ensures research compliance and reproducibility.
Scientific rigor in data analysis is validated by checking statistical validity, data integrity, and reporting accuracy. It ensures that effect sizes, confidence sizes, and sample sizes are correctly documented and that results are reproducible.
You can check statistical tests and sample sizes by running an analysis audit that identifies inappropriate tests, uncorrected multiple comparisons, and sample size issues. This validates your data validity and statistical assumptions.
Yes, you can use an automated tool to verify data integrity by checking outlier removal criteria, data transformations, and detecting synthetic data. This ensures your research compliance and analysis audit processes are robust.
Common data validity issues include missing sample size statements, incorrect confidence interval calculations, and failing to report effect sizes. An analysis audit checks these elements to ensure scientific rigor in your outputs.
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