My paper is in R&R but I have concerns about the data quality. What should I do? I am co-authoring a paper and I am the 2nd author on this paper. We submitted the paper and it got generally positive reviews. The reviewer comments were not difficult to address. I have put a lot of time into this paper, and I am in an early and important stage in my career, so publishing is important to me.
The issue is that I don't believe the results of our study. My coauthor has sent me the data to address some of revisions, and some of the effect sizes are uncomfortably large. I worry that there has been a severe methodological issue that is now impossible to diagnose. I have spent a long time trying to figure out what could be wrong, with no success. My concerns are limited to one study only; the other studies replicate this effect but do not have impossible large effect sizes. I have asked my colleague about this and am awaiting a reply. Revisions are due in a few days.
Presuming they want to proceed with the submission, my options (as I see them) are to:

Do nothing. The paper will likely get published and I can ignore it. The paper is in my field, but I don't plan to follow up on this particular effect.

Retract my authorship and let my coauthor go it alone. Have nothing to show for my effort. Risk making a rash decision based on something potentially minor-- but without discovering any new information, I may never know.


What should I do?
Talk to your co-author(s) openly about your concerns and ask for clarifications.
If such a direct confrontation seems uncomfortable to you, ask them to make the data openly available for anyone to replicate, e.g. at Zenodo, by referring to your university's internal regulations (I am sure there are guidelines about research transparency at your university). This should pressure them to ensure that the data are sound.
In addition, insist on making each co-author's contribution transparent in the form of CRediT (Contributor Roles Taxonomy). With this taxonomy, it will be public that you were not the one who handled the dataset, in case doubts should arise.