Audit
Stats Sanity Checker
Paste your Results section. We recompute every reported p-value and run GRIM, GRIMMER, and DEBIT in one go to flag inconsistent statistics, impossible means, and decision-flipping rounding errors before reviewers do.
We don’t train AI on your data. Pasted text uses short-lived result caching.
Paste your Results
APA-style stats prose, descriptives table, or LaTeX. 200-character minimum.
We extract every claim
NHST tests (t / F / χ² / r / z), descriptive triples (mean ± SD ± N), and binary proportions.
Math-checked flags
p-recompute (statcheck), GRIM, GRIMMER, and DEBIT. All in pure code, not LLM guesses.
Why stats integrity matters
Nuijten et al. (2016) scanned 30,000+ psychology papers and found that ~50% contained at least one inconsistent reported p-value, with ~13% containing a decision-flipping inconsistency. A 2024 follow-up by Nuijten & Wicherts found that integrating statcheck-equivalent checks into peer review correlated with a 4.5× reduction in reporting errors at submission.
Reviewers and meta-analysts run these checks routinely. The point of this tool is to put the same checks in front of you before submission, with plain-English flag explanations, so a fixable arithmetic error never becomes a public correction notice.
Limitations
- Reported stats only. We can recompute p only when test statistic + df + reported p are all present in the paste. We can’t check raw data we don’t see.
- Corrections suspend recompute. If you tick “Bonferroni / FDR / Holm,” we suppress p-recompute (the math is family-specific) and run only descriptive checks (GRIM / GRIMMER / DEBIT).
- GRIM range. GRIM is informative for N ≤ 200 with integer-bounded scales (Likert, counts). Above that, the rounding band swallows the scale and we surface “skipped” rather than false-positive.
- GRIMMER upper-bound only. V1 flags SDs that exceed the theoretical maximum given the integer scale and N. The full Anaya 2017 algorithm additionally checks integer-partition consistency; that fuller enumeration ships in a future update. Flagged cases are correct; some genuinely-impossible sub-maximal SDs may currently pass.
- Not a substitute for statcheck-on-PDFs. For batch checks of full manuscripts (PDF / DOCX), use the official statcheck R package or the upcoming statcheck Word add-in. Audit is built for the “paste my Results paragraph and tell me what’s wrong” loop.
Manuscript-level read
Want stats integrity checked in manuscript context?
The audit recomputes math from a paste. The full readiness scan reads your entire manuscript and flags missing power analyses, multiple-comparison gaps, methodology issues, and reviewer-flag patterns alongside arithmetic checks. Free preview, $39 only if you want the full report.
Calibrated from early work with 35+ CNS-experienced reviewers and senior scientists. Their reviewer language shaped how Manusights flags unsupported claims, weak evidence, journal-fit risk, and likely reviewer objections. Logos indicate researcher affiliation, publication context, or calibration-network context, not endorsement.
Want the math behind every flag? Read the full methodology · closed-form CDF formulas, severity classification, and citations to Nuijten 2016/2020, Brown & Heathers 2017, Anaya 2017, and Heathers 2018. Or read About + credits for the original tool authors we built on.
Picking a journal next? Run Manusights Compass · paste your title and abstract, get the top 5 best-fit venues with scope reasoning and a fit score.
Sanity-checking your bibliography too? Run Manusights Sentry · paste your reference list, get per-reference flags for retractions, expressions of concern, and hijacked-journal clones before reviewers screen them.
Already chose a target journal? Run Manusights Lens · returns a 0-100 scope-fit risk score for the venue you have already chosen, with named scope-fit patterns and lower-risk alternatives.