The short answer
Define a relevant historical cohort before judging the new post. Match the account, metric, distribution and observation age, then show the individual observations and a robust summary such as their median. The local worksheet audits declared ages and exclusions; it cannot verify the underlying analytics. This gives a personal comparison, not a universal good-impressions threshold.
What you’ll take away
- Keep the target post out of the history used to judge it.
- Observed zero, missing data and excluded observations mean different things.
- A median does not repair biased selection or mismatched topics.
Choose the comparison before seeing the answer
“Good” needs a purpose and a comparator. If the question is whether a new explainer received more displays than your recent comparable explainers, your own eligible history is useful. If you want to compare with an industry population, you need a relevant dataset and cohort definitions. This guide has no original population-impression dataset and does not fabricate one.
Write the historical period, included post types, topic scope and observation age before selecting results. Keep the new target separate. Including it in the historical median lets the item being judged influence its own benchmark. Omitting inconvenient quiet posts creates another bias even if the arithmetic is correct.
Use observations with a shared meaning
Use impressions from the owner’s actual analytics, not a number reconstructed from likes. Record one eligible observation per historical post and keep its source reference privately. The worksheet uses local IDs rather than names or public links. Use the same account and exact metric definition throughout; the form cannot establish those conditions for you.
Compare similar time since publication. A 24-hour count and a lifetime count measured months later do not offer the same opportunity to accumulate displays. A small allowed window is a declared compromise, not a universal rule. If no earlier snapshot exists, do not reverse-engineer it from today’s total. Keep paid, mixed and unknown distribution out of an explicitly organic comparison.
Read an external benchmark without changing what is ranked
A follower-size table can provide context, but first check its unit, collection method, date range and observation age. A percentile of sampled posts is not automatically a percentile of creators. Repeated contributions from the same account can weight that account more heavily, and a large opt-in collection does not by itself represent every LinkedIn member. Keep publisher-reported figures attributed rather than relabeling them as your own measurements.
In a constructed five-post collection, creator A contributes four observations of 100 impressions and creator B contributes one of 1,000. The post median is 100. If you instead summarize each creator by their median and take the median of those two summaries, it is 550. Neither calculation is wrong; the unit changed. The local tool summarizes your entered post observations, not a creator population. These invented values explain weighting and are not an original industry benchmark.
| Check | Question to resolve | If it is unresolved |
|---|---|---|
| Unit | Posts, creators or distinct reached accounts? | Do not turn a post percentile into a creator rank. |
| Clock | Same observation age and stated calendar window? | Keep the comparison descriptive and disclose the difference. |
| Distribution | Organic, paid, mixed or unknown? | Do not silently merge different acquisition paths. |
| Selection | Which accounts/posts were eligible, missing or repeated? | Record the limit; a large count does not remove it. |
Follow the full constructed example
The tool’s explicitly loaded example has historical organic counts of 100 at 24 hours, 200 at 23 hours and 300 at 25 hours. With a target age of 24 and tolerance 2 hours, all three qualify. A paid row with 10,000 impressions, a 168-hour row with 9,000, and a row missing its impression value remain visible as exclusions. These are invented exercise inputs, not creator data.
Their median and mean are both 200. A separate target of 250 is 1.25 times the median. Two historical values are below it and one above. That is a comparison with three entered observations, not a percentile of LinkedIn, a reach guarantee or a finding that the target caused more business value.
| Comparison choice | Constructed result | Interpretation |
|---|---|---|
| Organic, 22–26 hours | 100, 200, 300; median 200 | Three observations meeting declared rules |
| Separate target | 250 at 24 hours | 1.25× the historical median |
| Omit one history row at a time | Remaining medians 150–250 | Sensitivity to one included post |
| Paid / old / missing rows | Retained in the exclusion audit | Not silently treated as eligible or zero |
Check how much the summary depends on one item
NIST explains why extreme values affect the arithmetic mean more than the median. Inspect both and the full range rather than choosing whichever supports a preferred story. The tool also recomputes the median with each historical observation omitted in turn. For the example, the resulting range is 150–250.
This leave-one-out range is a sensitivity check, not a confidence interval or a population estimate. It cannot account for all unobserved posts or selection bias. With only one historical observation, no such check is provided. With an all-zero historical median, the ratio to that median remains undefined; actual zeros are retained.
Keep exclusions and source context with the result
Paste rows as local ID, impressions, whole-hour observation age and distribution code. Blank values remain missing. Nonempty malformed values, duplicate IDs and a target ID repeated in history stop the calculation rather than being discarded. The audit records why a valid row was excluded. An all-excluded input does not produce a guessed baseline.
Download the row audit and keep the target settings and source context with it. Labels and time windows are your declarations; the tool does not connect LinkedIn or establish that two posts addressed the same readers. Wider tolerances, cherry-picked periods and incomplete history can change the comparison. Do not widen rules after seeing a disappointing result without recording the change.
Use the comparison to choose a proportionate next step
If the new post differs from the baseline, first read its content and replies alongside comparable originals. A new topic, mention, audience or distribution event can explain the difference. A single relative count does not identify a platform penalty or prove that a format worked.
Report the bounded finding: “At the declared age, this post had 250 impressions versus a median of 200 across three eligible historical observations; three other rows were excluded for stated reasons.” Then name the next question. No amount of decimal precision turns that small personal history into an industry benchmark or a causal test.
Sources & product references
Product details reflect the publication date. External features and interfaces may change.
