> **Methodology in brief.** First-party LinkHub data: **657,786 real LinkedIn comments** whose impressions and replies were measured. We segment by a simple criterion: does the comment contain a question mark "?" or not. We report, per comment, average replies, average impressions and average likes. Complemented by public industry studies, cited and dated. The analysis is **correlational, not causal** — see the honest reading in §2.

## Key takeaways

- **A comment that asks a question gets +23% more replies**: **0.86 replies** on average versus **0.70** for a comment without a "?". *(LinkHub, n = 657,786)*
- The same question-comment also generates **+40% more impressions**: **242** on average versus **173**. Conversation drives redistribution.
- **Likes** barely move (0.94 vs 0.90): the question acts on **conversation**, not on passive applause.
- Only **~9% of comments** contain a question — a simple and widely underused lever.
- **Caveat**: correlation ≠ causation. A *relevant* question works; a throwaway question tacked on at the end does not.

## 1. Question vs no question: the numbers

Across **657,786 real comments**, we split two populations: those containing a question mark "?" and those that do not. The contrast is sharp on **conversation** and **reach**, near-zero on likes.

| Comment type | Avg. replies | Avg. impressions | Avg. likes | Sample |
|---|---|---|---|---|
| **With "?"** (≈9%) | **0.86** | **242** | 0.94 | 57,417 |
| Without "?" | 0.70 | 173 | 0.90 | 600,369 |
| **Gap** | **+23%** | **+40%** | +4% | — |

**Reading.** Asking a question in your comment is associated with **+23% more replies** (0.86 vs 0.70) and **+40% more impressions** (242 vs 173). On likes, the gap is marginal (+4%): the question does not generate more passive approval — it generates **conversation**. That is exactly the lever the algorithm rewards most (see our [comments vs likes](/en/blog/commentaires-vs-likes-linkedin) study and the [LinkedIn algorithm mechanism in 2026](/en/blog/algorithme-linkedin-2026)).

## 2. Why a question earns more replies (and more impressions)

The chain is mechanical:

- **The question calls for an answer.** Aimed at the post's author or other commenters, it creates a soft conversational obligation: people answer a question more readily than a statement. Industry studies confirm it: a question format acts as a natural "call-and-response" ([Sprout Social, 2026](https://sproutsocial.com/insights/linkedin-algorithm/)).
- **Replies trigger redistribution.** When a discussion thread forms under a comment, LinkedIn pushes it beyond the initial audience → hence the **+40% more impressions** observed. Conversation is the fuel for reach — the same mechanic applies to [replying to the comments on your own posts](/en/blog/repondre-commentaires-ses-posts-linkedin).
- **Methodological honesty.** Our data is **correlational**. Buffer, across 72,000 posts, is also transparent: conversational content is observed to perform better, without strictly proving causation ([Buffer, Jan 2025](https://buffer.com/resources/linkedin-engagement-data/)). A good question-comment is above all **relevant**: it is not the magic "?", it is the question that genuinely opens the discussion. A question forced in artificially ("Right?") will not reproduce the effect.

## 3. Should you always end your comment with a question?

No — and that matters. The measured effect (+23% replies) comes from **real** questions that extend the post's idea or sincerely engage the author. A few guideposts:

- **A question that adds an angle** > a bland closed question. "How do you handle this when the client says no?" beats "Do you agree?". For phrasings that work, see our [comment examples](/en/blog/exemples-commentaires-linkedin) and the method to [write a good comment](/en/blog/ecrire-bon-commentaire-linkedin).
- **Pair it with useful length.** A comment that is too short has no room for a good question; see our study on [comment length and impressions](/en/blog/longueur-commentaire-linkedin-impressions).
- **Relevance first.** The "?" is only a proxy. What matters is opening a real conversational loop — the one the algorithm redistributes.

That is exactly what LinkHub helps with: spot the right posts and write a relevant comment — one that asks the right question when it helps — with [personalized AI comments](/en/features/ia-commentaires-personnalises), always approved by you before publishing.

## 4. The most underused lever

The most striking finding: only **~9% of comments** contain a question. The vast majority assert, congratulate or summarize — without ever re-opening the conversation. Yet the re-opening is what turns a comment into a discussion, and a discussion into reach.

On your next comments, the experiment is easy to reproduce: when the topic allows, end with a sincere question and watch the reply count. To go further, browse our other [LinkedIn data studies](/en/blog).

## FAQ

**Does asking a question in a LinkedIn comment really get more replies?**
Yes: across 657,786 comments, those containing a "?" get **0.86 replies on average versus 0.70** without a question — that is **+23%**.

**And on impressions?**
Question-comments generate **+40% more impressions** (242 vs 173). The conversation triggered pushes the algorithm to redistribute.

**Is this causation?**
No, it is **correlation**. A relevant question opens a conversational loop; a throwaway question tacked on at the end does not reproduce the effect.

**Should you end every comment with a question?**
No. The effect comes from real questions that extend the post. Favor relevance over reflex.

**How do I find the right posts to ask these questions on?**
Through feeds targeted on your prospects and creators in your niche. See our [AI profile recommendation](/en/features/ia-recommandation-profils).

## Sources & methodology

- **LinkHub dataset** — **657,786 comments** with measured impressions and replies, segmented by the presence of a "?" (with: 57,417; without: 600,369). Correlational analysis.
- [Buffer — Replying boosts engagement by 30% (72,000 posts, Jan 2025)](https://buffer.com/resources/linkedin-engagement-data/) · [Sprout Social — LinkedIn algorithm 2026](https://sproutsocial.com/insights/linkedin-algorithm/)