A revise-and-resubmit is an invitation, not a rejection. The statistical comments are usually the hardest to answer — and the ones where a defensive response does the most damage. Here is how to answer them cleanly.
Reviewer 2 is a stand-in for the skeptical reader you will never meet. The goal of a response letter is not to win every point; it is to show the editor that each comment was taken seriously and resolved — by a change, by an added analysis, or by a reasoned, evidenced disagreement. Three moves carry almost every statistical response: concede and fix, concede and reframe, or disagree with evidence. What sinks a revision is a fourth move — deflect — where a real limitation is met with a paragraph that talks around it. Reviewers read deflection as a tell.
Quote each comment verbatim, answer directly beneath it, and point to the exact revised location (page and line). What follows are the statistical critiques that recur, and language that answers each without spin.
This is the most common statistical objection, and the one most often answered badly — by repeating that the result was “not significant” as though that settled it. It does not. An underpowered null is a failure to detect, not a demonstration of absence.
How to answer. Concede the imprecision and requantify. Report the confidence interval and state what effects it still admits; add a minimum detectable effect or, if an equivalence claim is intended, a pre-specified margin. Then soften the wording everywhere the null appears. A model response: “The reviewer is correct that the study was not powered to establish equivalence. We now report the 95% CI (reoperation OR 1.03, 0.76–1.38), note that it does not exclude a clinically meaningful difference, and have changed ‘was not associated’ to ‘no significant association was detected’ throughout (p. 9, lines 4–11).” The mechanics of this fix are worked through in why manuscripts get rejected for statistics.
How to answer. You have two honest routes. Either apply a correction and report what survives, or — often better — declare the analyses exploratory and label them as hypothesis-generating rather than deleting them. What you cannot do is keep presenting a handful of p < 0.05 results from twenty tests as confirmed findings. A model response: “We agree. The primary endpoint remains as pre-specified; the fifteen secondary comparisons are now explicitly labeled exploratory, and we have added that with α = 0.05 roughly one significant result is expected by chance (Methods, p. 6).”
Missing data. If the answer is complete-case analysis, say so and add a sensitivity analysis — multiple imputation or a missing-indicator check — showing the conclusion holds (or noting honestly where it wavers). Report the excluded count in the main text, not the supplement.
Confounding. For an observational study, you usually cannot randomize your way out in revision. What you can do is add whatever risk adjustment the data support, report an e-value for how strong an unmeasured confounder would need to be to explain away the effect, and state the direction of likely residual bias. A model response: “We cannot fully exclude confounding by indication. We have added adjustment for a comorbidity index, report an e-value of 1.7, and now state that residual confounding would most plausibly bias the association toward the null (Discussion, p. 12).”
This comment is nearly always cheaper to satisfy than it looks, because the fix is wording, not re-analysis. If the primary endpoint was negative and the abstract leads with a secondary one, that is spin and a reviewer will name it. Pull the conclusion back to the primary result and let the abstract say plainly what was and was not shown.
How to answer. “We have revised the abstract and conclusion so the primary (negative) endpoint leads, and reframed the secondary finding as hypothesis-generating (Abstract; Discussion, p. 13).” Conceding an over-claim costs you nothing an editor values and buys you the credibility to hold your ground elsewhere.
The strongest position in a revision is having already named the limitation the reviewer raises. Every comment above is one you can anticipate before submission — the underpowered null, the unadjusted multiplicity, the silent missing data, the conclusion that drifts. Naming them yourself, in the limitations and in calibrated wording, is far stronger than having a reviewer find them unnamed.
A structured read surfaces these before a reviewer does. RigorMD returns a severity-scored report grounded in your own quotes, with before/after language suggestions for the exact sentences a reviewer would flag — and, for a manuscript already reviewed, a revision read that maps reviewer comments to point-by-point guidance. It flags for your judgment; it does not certify, write your response letter, or guarantee acceptance. See a full sample report → (the language section shows the before/after wording), read how the engine works, or review pricing — the pre-submission review is $25, and the post-review revision read is $10 for a study already reviewed here.