Pre-submission statistical review

Catch the statistical error before your reviewer does.

Independent review of your analysis, tables, and reporting — flat pricing by complexity, a structured report mapped to your manuscript, no open-ended hourly clock.

Results — Study 2
manuscript_draft_v3.docx · reviewed

A repeated-measures ANOVA was conducted (F(2, 42) = 4.81, p < .05). Effect sizes were reported as partial η² = .19, 95% CI [.04, .34], consistent with reporting guidelines. Post hoc comparisons were adjusted using no stated correction method.

Flag · df mismatch Table 2 reports N = 39 (df = 38), but text states df = 42. Confirm which N was used.
Passed Effect size and CI reporting meets APA 7th edition standard.
The problem

Reviewers reject papers over things that have nothing to do with your findings.

These are the errors that get flagged in review — quietly fixable before submission, publicly embarrassing after.

01

Mismatched degrees of freedom between text and tables

02

Assumption violations never checked or never reported

03

Effect sizes reported inconsistently across the manuscript

04

Multiple comparisons run without correction or disclosure

Process

How it works

Upload your manuscript

Send the full paper, or just the methods and results sections, through a secure link.

Get a scoped quote

A flat price based on analytic complexity, within 72 hours. You approve before any review starts.

Receive a structured report

Within 5 business days, every issue mapped to its exact page and table, with a suggested fix — not just "this is wrong."

Optional revision call

Walk through the findings together before you submit.

Pricing

Flat pricing by complexity

One price, agreed before work starts. No hourly billing.

Basic
$400–600
t-tests, ANOVA, simple/multiple regression, single-study designs
  • Assumption checks (normality, homogeneity of variance, independence)
  • Effect size & CI reporting review
  • Table/text consistency check
Complex
$1,500–3,000
SEM, CFA/IRT, growth curve models, multi-study manuscripts, psychometric validation
  • Everything in Moderate
  • Model fit index review
  • Measurement invariance review
  • Cross-study consistency check
Not sure which tier fits? Send your methods section — you'll get a scoped, flat quote before any work begins.

See a real report before you commit.

A redacted sample shows exactly what you get: each issue located in your manuscript, why it matters, and the fix — not a chat transcript.

Download Sample Report
FLAG · p.4 — sphericity not tested for RM design
PASSED · p.6 — CI reporting meets APA 7
FLAG · Table 3 — N inconsistent with Method
PASSED · p.9 — model fit indices complete
About

Not a generic AI review tool.

Reviewer

Nedim Yel, PhD

Measurement & Statistics · M.S. Teaching Physics

RPythonSPSSSASStataSQL

15+ years reviewing and analyzing data across IES- and NSF-funded research, spanning school psychology, counseling psychology, math education, computer science education, and law education.

AI helps this move fast — the checklist and the judgment are not automated. Every report reflects a direct read on what reviewers actually flag, built from years inside grant-funded research design and analysis.

FAQ

Questions

AI can scan for surface-level issues. It won't reliably catch that your reported df doesn't match your N, or that a repeated-measures design needed a sphericity check it didn't get. This service is judgment-driven — AI helps move fast, but the checklist and the read are mine.

You get a short report confirming what checks were run and passed — useful documentation for a methods reviewer or committee, and cheap peace of mind before submission.

Yes. Manuscripts and data are used solely to complete the review, are never shared with any third party, and are deleted from all systems within 30 days of report delivery. A signed confidentiality agreement or NDA is available on request before any file is submitted.

That's a different, larger scope — reach out and it'll be quoted separately from the standard tiers above.