Respond to Reviewers Example: 15 Real Scenarios & Templates

Statistical comments are common because they are one of the fastest ways reviewers test whether the paper's logic is as strong as the authors think it is. They often suggest different tests or question the sample-size logic without fully seeing the analytic plan you followed.

Template response:

"Comment: The authors should use ANOVA instead of t-tests given the multiple comparisons."

"Response: We appreciate this suggestion; however, we conducted planned comparisons between specific groups rather than an omnibus test, which makes paired t-tests with Bonferroni correction the appropriate approach (Maxwell & Delaney, 2004). Our comparisons were theoretically motivated and specified a priori in our preregistration (available at [URL]). We've added a sentence to clarify this rationale:

'We used planned comparisons with Bonferroni correction (α = 0.017) rather than omnibus ANOVA because our hypotheses specified directional predictions between particular groups (Smith et al., 2018).'"

When they question sample size: Don't get defensive. Show your power analysis or cite similar studies. If your sample is small, acknowledge it honestly and explain why it's still valid for your specific question.

Sometimes honest limitations beat forced justifications. Especially when you're dealing with reviewers who've seen every excuse in the book.

Notice the structure here: acknowledge the concern, explain your reasoning with citations, show exactly what you've changed in the manuscript. This pattern works for 90% of statistical disagreements; saves you revision rounds, and demonstrates the kind of scientific rigor that editors want to see in their journals.

Some reviews are openly hostile. The trick is to separate tone from substance and answer the valid scientific point without mirroring the aggression.

Template for hostile comments:

"Comment: This work is fundamentally flawed and shows the authors don't understand basic principles of [field]. The methodology is amateurish and the conclusions are unsupported."

"Response: We appreciate the reviewer's concern about our methodological approach. We've strengthened our methods section to clarify our rationale and added validation experiments to support our conclusions. Specifically:

1) We added a detailed explanation of why we chose Method X over alternatives (Lines 145-152)

2) We included control experiments Y and Z to validate our approach (new Figure S3)

3) We revised our conclusions to better reflect the scope of our findings (Lines 320-328)

These changes address the core methodological concerns while maintaining the integrity of our original findings."

Never mirror their tone. Extract the valid scientific points from the hostility; address those points thoroughly. Don't defend your competence directly. Let your revisions speak for themselves.

The hostile reviewer tests your professionalism more than your science. Pass that test, and you'll often find the editor sides with you when the reviewer's tone was clearly inappropriate.

This is one of the most common hard cases: Reviewer 1 wants more detail, Reviewer 2 wants less, and you cannot satisfy both literally.

Template for contradictory demands:

"Comments: Reviewer 1 requests additional analysis of X. Reviewer 2 suggests removing analysis of X as unnecessary for the main findings.

Response: We recognize the reviewers have different perspectives on the importance of analysis X. After careful consideration, we've moved this analysis to supplementary materials. This preserves the detailed analysis requested by Reviewer 1 while maintaining the focused narrative preferred by Reviewer 2."

Decision framework: If both requests are scientific, pick the one that strengthens your paper. If one is stylistic and one is scientific, go with the science. When editors see you've found a compromise that satisfies both reviewers' underlying concerns, they usually accept your solution rather than sending the manuscript to additional reviewers.

Scope creep kills timelines.

Reviewers ask for new experiments, additional studies, or analyses that would require six months and double your budget. These requests sound reasonable until you calculate the actual work involved.

Template for scope creep:

"Comment: The authors should include a longitudinal study to validate these cross-sectional findings."

"Response: We agree that longitudinal validation would strengthen these findings; however, such a study would require 18-month follow-up data that is beyond the scope of the current manuscript. We've added discussion of this limitation (Lines 298-304) and identified longitudinal validation as a priority for future research. The current findings provide valuable insight into [specific contribution] that merits publication while this additional work proceeds."

Be honest about timeline and resources. Editors understand project constraints better than you think. They'd rather publish good science now than wait two years for perfect science that might never come; especially in fast-moving fields where your findings could become obsolete.

Template for citation requests:

"Comment: The authors fail to cite important work by Johnson et al. (2019) and Lee et al. (2020) on this topic."

"Response: We thank the reviewer for these suggestions. We've added Johnson et al. (2019) to our introduction as it provides important context for our theoretical framework (Line 67). However, Lee et al. (2020) focuses on [different population/method/outcome] which, while related, addresses a distinct question from our current work."

Don't pad your references. Quality citations strengthen your argument; quantity citations weaken it.

Sometimes reviewers suggest their own papers. Include them if they're relevant, but don't feel obligated to cite irrelevant work just to appease the reviewer. Editors can usually spot quid pro quo citations and they don't appreciate being manipulated through the review process.

For control experiment requests:

"Comment: The authors need negative controls to rule out non-specific effects."

"Response: We agree this control is essential and have added the requested negative control experiment (new Figure 2C). We tested [specific control condition] and found [specific result]; confirming that our observed effects are specific to [treatment/condition]."

For sample selection criticism:

"Response: This is a valid concern. Our sample was indeed [demographic breakdown] which limits generalizability to [specific population]. We've added this limitation to our discussion (Lines 312-318) and modified our conclusions accordingly."

The key to methodology responses? Show, don't just tell. Provide data, cite standards, and acknowledge limitations honestly. Reviewers trust authors who admit weaknesses more than those who claim perfection.

Writing issues are the easiest to fix but the most dangerous to ignore. Clear writing signals clear thinking; muddy prose suggests muddy science. Why risk rejection over fixable problems?

Template for clarity requests:

"Comment: The rationale for using Method X is unclear."

"Response: We've clarified our rationale by adding three sentences to the methods section (Lines 134-138): 'We selected Method X over alternatives Y and Z for three reasons: (1) Method X provides [specific advantage], (2) it has been validated for [your specific conditions], and (3) it allows direct comparison with previous studies in this field.'"

Defensive language that backfires:

• ❌ "The reviewer clearly misunderstood our methods."
• ✅ "We've clarified our methods to address this concern."

Dismissive responses:

• ❌ "This comment is outside the scope of our paper."
• ✅ "While this represents an interesting future direction, the current study focuses on [specific scope]."

Evasive non-answers:

• ❌ "We will consider this suggestion for future work."
• ✅ "We've added this analysis to supplementary materials and discussed the implications in Lines 245-251."

The general rule is simple: defensive language makes editors nervous, while controlled, specific language makes it easier for them to keep the paper moving.

1. Maxwell, S.E. & Delaney, H.D. (2004). Designing experiments and analyzing data: A model comparison perspective. Lawrence Erlbaum Associates.
2. Journal and publisher author guidance on revision letters, point-by-point responses, and reviewer communication.
3. Editorial best-practice guidance on responding to reviewer comments and revision decisions.