stats

To detect p-hacking and multiple testing issues, provide your research passage or results to validate statistical claims. The tool audits for multiple comparisons, identifies p-hacking patterns, and outputs a structured report with issue severity and remediation guidance.

The best way to check sample size adequacy is through methodology auditing, which validates effect size relevance and flags potential confounders in your manuscript. Submit your textual content or results to receive a structured report with severity levels and remediation guidance.

Yes, you can audit statistical claims using only textual content or results. The tool requires minimal inputs to validate claims against described methods, flagging fallacies like correlation vs causation confusion and providing remediation guidance without needing the raw dataset.

Yes, this statistical validation tool applies to papers, notes, and datasets across all academic disciplines. It universally flags methodological fallacies like underpowered samples and improper multiple testing, generating structured reports with issue severity regardless of the research field.

To identify correlation versus causation confusion, submit your research passage for fallacy detection. The tool specifically scans for this methodological error alongside p-hacking and multiple testing issues, returning a structured report detailing the issue severity and actionable remediation steps.

Common statistical errors include p-hacking, underpowered samples, correlation vs causation confusion, and improper multiple testing. The tool identifies these methodological fallacies by validating claims against described methods and outputs a structured report with issue severity and remediation guidance.