What Statistical Reviewers Actually Look For

Beyond the checklist

Statistical review is often imagined as a hunt for errors. In reality, reviewers are evaluating whether conclusions are trustworthy.

They focus less on novelty and more on defensibility.

What reviewers pay attention to

• Alignment between question, design, and analysis

• Transparency around assumptions and limitations

• Proportional interpretation of results

• Evidence of robustness and sensitivity checks

Technical correctness is assumed; judgment is assessed.

Common reasons analyses fail review

• Overstated conclusions

• Unacknowledged limitations

• Selective reporting of favorable results

• Lack of clarity around decision relevance

How teams can prepare

Approaching analysis with a reviewer’s mindset improves quality long before submission.

Responsible interpretation is often what separates accepted work from questioned work.

These observations strongly influence how we think about structured statistical support as InsightSuite takes shape.