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Yes, in the conferences studied, men asked a larger share of audience questions than women. The clearest audience-adjusted estimate comes from a 2017 study: in a hypothetical room with equal numbers of men and women, men were estimated to ask 64% of questions—about 1.8 questions for every one asked by a woman (95% confidence interval, 1.4–2.2). That is a result from particular sessions, not a universal rule about scientists.
Later observations in hematology, astronomy and a virtual biology-and-mathematics meeting found the same broad direction, although the size of the difference varied with field, format and measurement. The studies count participation; they do not show that men are inherently more curious, knowledgeable or capable.
What the strongest study actually measured
Amy Hinsley, William J. Sutherland and Alison Johnston analyzed question-and-answer sessions at a large international scientific conference in 2017. Their main analysis covered 31 sessions and 270 questions: 152 questions came from men and 118 from women. The researchers also estimated the audience in those sessions at 1,116 men and 1,487 women.
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A simple count would compare 152 with 118, but that would ignore the fact that more women than men were estimated to be in the audience. The authors therefore modeled question rates per attendee and included each session’s male-to-female audience ratio. They estimated an average of 0.14 questions per male attendee and 0.08 per female attendee. Adjusted to an evenly balanced audience, men would ask 64% of questions, or 1.8 questions for every question asked by a woman.
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The paper’s abstract states: “Accounting for audience gender ratio, male attendees asked 1.8 questions for each question asked by a female attendee.” The estimate has a 95% confidence interval of 1.4–2.2, which expresses statistical uncertainty around that conference-specific model. The study was observational: delegates were not told they were being observed, an approach intended to reduce changes in ordinary behavior.
The analysis used a binomial generalized estimating equation, treated session as a repeated measure and used audience composition as an offset. That session-level adjustment is important because audience mix can differ substantially from one room to another. Adding speaker or chair gender did not meaningfully improve the model fit in this dataset.
Results from other conference settings
Several studies provide corroboration, but their percentages and ratios should not be pooled. They examined different disciplines, years, formats and definitions of a questioner.
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|---|---|---|---|
| American Society of Hematology annual meeting, 2019 (published 2020) | 112 sessions and 577 presentations | Women asked 23% of audience questions, compared with an estimated 39% of attendees (P < .0001). | The attendee denominator was estimated for the conference rather than directly measured for every room. |
| JOBIM virtual conference, 2021 (published 2023) | 192 chat questions | 57 were attributed to women, 115 to men and 20 were anonymous or group submissions. | Typing a question into a chatbox is not the same behavior as approaching a microphone in a physical room. |
| American Astronomical Society meeting, 2014 | 634 questions from 225 talks in 53 sessions | 153 questions were attributed to women and 481 to men. | This was an informal volunteer sample; gender assignments were binary and sometimes inferred from first names. |
| Three academic conferences (published 2022) | 189 observed live Q&A interactions and 234 survey respondents | Men disproportionately participated in recorded live Q&A. Women survey respondents reported less comfort and more fear of backlash. | Survey reports describe experiences and perceptions; they do not by themselves establish why observed counts differed. |
| International conference study (published 2025) | 388 questions after 134 talks and 373 complete surveys | Women showed lower participation in the conference studied. | The observational and self-report components answer different questions and should not be treated as one causal test. |
The pattern is therefore replicated across several contexts, but the numerical results are not interchangeable. A 23% share at a hematology meeting, a 1.8-to-1 audience-adjusted rate at one international conference and a chat count from a virtual event use different denominators and procedures.
Why audience adjustment changes the interpretation
Suppose a conference has twice as many women as men in the room. If men and women ask the same number of questions per person, women could still produce more questions in absolute terms because there are more women present. Conversely, a raw majority of male questioners can understate or overstate a participation difference depending on who attended each session.
The 2017 study addressed this by estimating a question rate for each gender within sessions, rather than relying only on the conference-wide audience balance. Its 1.8 figure means “per attendee under the study’s model,” not “men in general ask 80% more questions at every conference.” The confidence interval also reflects uncertainty in the estimate.
The hematology study used a different comparison: women’s share of questions versus an estimated share of conference attendees. Because the attendee figure was not a direct gender headcount in every room, it answers a less granular question than the 2017 session-level model. The astronomy project likewise offers historical disciplinary context, but its volunteer collection and name-based coding make it less controlled.
What these findings do—and do not—say about gender
They describe participation, not ability
None of these studies tested scientific knowledge, intelligence or curiosity. They observed who asked a question under particular social and logistical conditions. A lower question count cannot be converted into a claim that women had fewer ideas or understood less of the talk.
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They do not establish a single cause
The 2017 authors discuss the history of demographic imbalance and broader barriers in science. Their analysis of younger researchers also found a difference, so seniority alone cannot explain the result. The same study found little evidence that speaker or chair gender improved its statistical model.
The informal astronomy report found an association between chair gender and the balance of questioners, while the 2017 analysis did not find a meaningful chair or speaker effect. Those results are compatible with a context-dependent influence, but they are not a settled causal finding.
Comfort and anticipated backlash are plausible context
In the 2022 study, women who answered the survey reported less comfort participating and greater fear of backlash. The 2025 study also combined observations with self-reports about the conference environment. These findings make perceived climate and social risk relevant factors to investigate. They do not prove that fear of backlash explains every difference, or that all women experience the same barrier.
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Gender categories limit what can be concluded
Several studies coded people as men or women based on observation, records or names. The 2017 and astronomy analyses explicitly used binary categories, and name-based inference can misclassify people. Such methods provide no reliable account of nonbinary and gender-diverse attendees. A two-category result should not be presented as a complete map of participation by everyone at a conference.
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How each method affects the headline number
- Raw question counts: Easy to report, but they ignore how many people of each gender were present.
- Audience-adjusted rates: More informative when audience composition is measured for each session, as in the 2017 analysis, but still dependent on the study’s gender coding and model.
- Estimated conference-wide denominators: Useful for a large meeting, yet less precise when room-by-room attendance differs, as in the hematology comparison.
- Volunteer samples: Can reveal a striking pattern, but talks or sessions may not represent the whole meeting. The 2014 astronomy data fall into this category.
- Live microphone observations: Capture hand-raising and public speaking, including social pressures that do not occur in the same way online.
- Chatbox submissions: Increase anonymity and may lower the cost of asking, but anonymous or group entries cannot always be assigned to an individual.
- Surveys: Reveal comfort, perceived risk and attitudes that observers cannot see, while relying on self-report and respondent participation.
The JOBIM study illustrates the classification issue directly: 20 of its 192 questions were anonymous or submitted by a group. Assigning all questions to only “men” or “women” would have discarded information or created a false attribution.
What conference organizers can do with the evidence
The studies do not prescribe one intervention, but they point to practical ways to make participation easier to study and to access:
- Track the denominator. Record attendance by session, not just total registration, if you want to compare question rates fairly. Explain how gender information was collected and what categories were available.
- Offer more than one route to ask. A moderated microphone, written cards and a question chat can reach people who face different levels of public-speaking pressure. Keep anonymous and group submissions identifiable as such in the dataset.
- Set expectations for the room. A chair can state that concise, topic-focused questions are welcome, interrupt personal attacks and prevent a small number of people from taking every turn.
- Use a transparent queue. Alternating among different parts of the room or using a visible queue can reduce reliance on repeated hand-raising. This is a facilitation choice, not evidence that any gender is less willing to participate.
- Evaluate the intervention. Compare participation before and after a format change using the same definitions. Separate observed counts from survey responses instead of combining them into one score.
- Protect privacy. Do not publish identifiable gender classifications based on appearance or names. Report uncertainty and acknowledge people who could not be classified.
These steps address access to the Q&A without assuming that a lower observed rate reflects lower interest. They also make future comparisons more credible.
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When a headline says that men asked more questions, check six details before interpreting the number:
- Which conference, discipline and year were studied?
- Were the data from an in-person microphone, an online chat, written submissions or a mixture?
- How many sessions, talks and questions were included?
- Was audience gender measured in each session or estimated for the whole meeting?
- How were gender categories assigned, and were anonymous or group questions retained?
- Is the result observational, self-reported or both?
A result that survives these questions is still a result about that setting. It becomes stronger evidence when independent studies use compatible definitions and sampling, not merely when a larger raw count appears in a news headline.
What can responsibly be concluded
Across the specific scientific and academic conferences studied, men asked more audience questions relative to their representation than women did. The best-known estimate is the 2017 audience-adjusted 1.8-to-1 rate, supported in direction by studies of hematology, astronomy, virtual conferences and later multi-conference work. The magnitude differs by field, format, denominator and method, and the evidence does not identify one universal cause.
Hinsley, Sutherland and Johnston conclude: “We encourage the scientific community to recognise the context in which these gender differences occur, and evaluate and develop methods to support full participation from all attendees.” That is the most defensible takeaway: the participation gap is observable in several settings, but it should be interpreted as a feature of particular conference environments—not as a measure of innate scientific interest or ability.
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