In this revision, we address reviewer comments from Dennis Leung and Nancy Heckman. We greatly appreciate the reviews and we thank the reviewers for taking the time and trouble to read and comment on the manuscript.

Review by Dennis Leung:

• I would like to play devil's advocate here and point out the possibility that the power of the randomization test employed in this paper may simply not be high enough to detect the difference in median between women and men. For instance,  there could be combining functions other than the Fisher Omnibus one to choose from, and the optimal permutation test is generally hard to nail down (p.107 in Pesarin and Salmaso). Another limitation of the current study, as also pointed out by the authors, is that the dataset is not large enough to stratify by year to allow for more fine-grained analysis. 

In many departments, women did not get more questions than men (e.g., ME and Physics), so the issue of statistical significance does not even arise. Also, none of the individual P-values are small, so Fisher’s combining function is likely to have more power than, say, Tippett’s. The size of the dataset is certainly a limitation, as discussed in Section 5.4.

• The use of certain terminology could have been more consistent to increase readability, e.g. "overall talks" vs "entire talks" (p.8 and p.9).

We have changed “overall talks” to “entire talks” to increase consistency.

• Table captions could have been extended to describe the content of the table better, e.g. In Table 1, it wasn't apparent to me until later on seen in Section 5.2 that "median events" refers to median number of audience utterances. 

We have revised the Table 1 caption to clarify the meaning of “median events.”

Review by Nancy Heckman:

• The only criticism one can make is that, sadly, randomization methods don’t have as much power as parametric methods.  This may be the reason that the paper’s results were null – no differences.

As discussed above and in the manuscript, in many departments women did not get more questions than men (e.g., ME and Physics). Section 5.4 discusses our conclusion that  the observed differences are not material, regardless of whether they are statistically significant. As discussed in Section 4 and the appendix, we believe that the finding of significance via parametric methods is actually an artifact of incorrect assumptions, not better power. Furthermore, randomization methods do not always have less power than parametric methods, especially if the parametric methods are calibrated for validity.

• One part of the paper – a very small part - was a little disappointing, and stood in contrast to the rest, which was so carefully laid out.  This is in section 5.1 “Are interruptions bad?”  This is a very interesting question, of course. The authors write “Table 1 shows that the proportion of female pre-tenure faculty in CEE, EECS, and IEOR is higher than the proportion of women in their applicant pools. These departments also spent more time questioning women than men.”  I’m not sure what I can take from this. The statement relates past hiring practices with current interviewing practices, which is a questionable way to consider the question “Are interruptions bad?”.  It seems that the CEE department data provides a more direct way to answer the question, as we read “In CEE, faculty presenters who received offers generally were asked more questions during their talk than presenters who did not receive offers.”   There is no statistical analysis here, which is OK, I guess, since all of this is in the discussion.  But I feel that the authors should put some cautionary remarks here about making any conclusions. 

The data on the proportion of pre-tenure faculty and the proportion of women in their applicant pools span similar time periods to our interview data. We have clarified this in section 5.1.

We have added more discussion to the CEE offer data discussion (Section 5.1) and put in a disclaimer that future studies should be conducted to investigate this relationship.