I was recently asked for a minor revision a review paper submitted, including more detailed reporting on parts of the analysis. When performing this additional analysis, I noticed a big data extraction error which had a small effect on the results and conclusions. It has no relevance to our main conclusion. This wasn't picked up by the peer reviewers, who were generally very positive about the paper and I think it has a good chance of being accepted.

Obviously I intend to correct this error and the conclusions derived from it. What is the proper way to go about disclosing this? My plan was to include this as a general note to all reviewers at the start of the response document before addressing all of their individual comments. Should I explictly point this out to the reviewers and explain, or just fix it without explanation and only address their comments directly in the response?

Any and all suggestions much appreciated.

2 Answers 2


It is probably better to point it out. A note to the editors/reviewers is fine. You and everyone want the best science to be published. Full disclosure is best.

I doubt that this would lessen the chance of acceptance, but it might draw a bit more scrutiny overall. But that is a good thing in general.

This is similar to pointing out other changes in the paper, those that were called for and those that you think are relevant to improving the paper.


In your response to reviewers, you can add a section at the end titled "Other changes" or something like that. Explain this data extraction error and mention that it does not change the results to any noticeable extent.

I think that by putting it at the beginning, you're implicitly saying that it's a bigger issue than it really is. If it really does not invalidate the peer review process thus far, it is more a footnote than a main point.

I would also triple-check your other data extractions. While one slip-up is unfortunate, two might signal that you're not careful in your methodology. You could specify that you have verified all your results in your response as well, if you fear that reviewers would doubt your results.

  • +1 for the last paragraph: explain how you made sure that the current data extraction doesn't include another big mistake (which might affect the results...)? Commented Feb 8, 2021 at 19:33

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