statistical-review

You can automate statistical review by uploading a manuscript to run a standardized quality audit that checks test appropriateness, assumptions, and effect-size reporting, generating a standards-aligned QA report.

P-hacking detection in manuscript audits identifies selective reporting or manipulation of data analysis by flagging multiple testing issues and verifying corrections across medical, social, and STEM disciplines.

Statistical review checks test appropriateness by evaluating each outcome type and study design, verifying that the selected tests align with methodological standards before peer review submission.

Yes, automated statistical audits are applicable across medical, social, and STEM disciplines for pre-submission checks and methodological audits to improve rigor and reproducibility.

Effect size reporting verification evaluates manuscripts to ensure effect sizes are reported with confidence intervals as part of a structured statistical quality audit workflow.

Automating statistical rigor checks supplements but does not replace human peer review, as it focuses on structured evaluation steps like assumption verification and missing data handling without interpreting domain-specific context.