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AI Paper Error Detector

Detect mathematical errors, statistical issues, citation problems, and data inconsistencies in academic papers. Free, no sign-up required.

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Check For

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Detected issues will appear here

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Math Verification

Checks calculations, equations, and numerical results for accuracy.

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Statistical Review

Identifies p-value issues, sample size discrepancies, and statistical claim mismatches.

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Citation Check

Verifies citation format, completeness, and reference consistency.

How to Use AI Paper Error Detector

Check your academic papers for common errors:

  1. 1Paste sections of your paper — methods, results, equations, tables, or references.
  2. 2Select the types of errors to check: math, statistics, citations, logic, or data.
  3. 3Click 'Detect Errors' — the AI scans for calculation mistakes, statistical inconsistencies, and citation issues.
  4. 4Review each detected issue with severity level, location, and suggested fix.

Who Is AI Paper Error Detector For?

For anyone who writes, reviews, or reads academic papers.

Researchers

Check your own papers before submission to catch errors that reviewers might flag.

Peer Reviewers

Quickly identify potential issues in manuscripts you're reviewing.

Graduate Students

Learn to spot common errors in academic writing and improve your own work.

Journal Editors

Initial screening tool to identify papers with obvious methodological issues.

Frequently Asked Questions

What types of errors can it detect?
Mathematical calculation errors, statistical inconsistencies (p-values, sample sizes), citation problems, logic flaws, and data discrepancies between text and figures.
Is it a replacement for peer review?
No. It's a supplementary tool to catch common errors. Peer review remains essential for thorough evaluation.
How accurate is the detection?
The AI identifies potential issues based on patterns. All findings should be verified manually before taking action.
Can I use this for published papers?
Yes. Researchers have used similar tools to find errors in published studies, contributing to scientific integrity.

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