Check AI-Generated Statistics Before They Reach Your Blog

A precise number can make an AI draft look researched even when its source is missing or its meaning has changed. Before publishing a statistic, verify not just that the number exists, but that it supports the sentence you intend to write.

Trace the number to its origin

Open the original study or dataset when available. A roundup may repeat a result accurately while omitting its sample, date or limitations. Record who collected the data, what was measured and whether the source has a commercial interest relevant to the claim.

If the original cannot be found, either identify the statement as a secondary report with its limitations or remove the statistic. A plausible citation generated by AI is not enough.

Check four parts of the claim

1. Population: Which sites, people, queries or sessions were included?
2. Denominator: A percentage of what, exactly?
3. Time: When were observations collected, rather than when the article was updated?
4. Method: Was this an experiment, an observational comparison or a survey?

Keep the numerator and denominator together in your notes. “Ten conversions” and “a 10% conversion rate” cannot be evaluated in the same way without knowing the underlying counts.

Worked example: a convincing but weak percentage

Imagine a hypothetical AI referral report with four signups from 20 sessions. The observed signup rate is 20%. A draft might describe this as evidence that AI traffic converts exceptionally well.

That conclusion runs ahead of the data. Twenty sessions are a small sample, a signup is not necessarily a paying customer, and the visitors may have arrived with unusually strong intent. Report the counts and the event definition; do not present the percentage as a forecast.

Watch for quiet changes in meaning

A rise from 2% to 3% is one percentage point and a 50% relative increase. Both are mathematically correct, but they answer different questions. Include the starting and ending values to prevent ambiguity.

Publish a useful interpretation

Explain what the result changes for the reader and what it does not establish. Sometimes the most useful conclusion is that the evidence is too narrow to justify a major investment.

Store the source and checked date beside the claim. If the statistic is central to the article, schedule a review based on how quickly the underlying subject changes. Decorative numbers that do not help a decision are often better removed entirely.

Why models produce convincing numbers

An assistant asked to "add supporting statistics" will oblige, because that is the task it was given. The figures it produces come from three places, and each needs different handling:

• A real study, reported accurately. The best case, and still worth opening, because the article may need a different part of the study than the one summarized.
• A real study, blended with another. The percentage from one report attached to the sample size or year of a second. Each detail is real; the combination is not.
• A pattern-shaped number. No source exists. The figure matches the typical scale of numbers in similar articles, which is exactly why it looks right.

The third case is not rare, and a request for a citation does not eliminate it: the model can produce a plausible title, author, and year with equal fluency. The only reliable test is to find the source yourself and read the passage. If the passage cannot be found within a few minutes, treat the number as unsupported and move on.

A ten-minute verification routine

Most statistics in a draft can be checked quickly if the checks are done in a fixed order. This routine has been practical for editors handling several drafts a week:

1. Search for the exact figure plus one distinctive noun from the claim. If every result is a secondary roundup, keep going until you reach the original.
2. In the original, find the table or sentence that contains the number. Copy its wording into your notes with the page or section reference.
3. Confirm the four parts: who was measured, what the denominator is, when the data was collected, and what method produced it.
4. Compare the draft's sentence with the source's sentence word by word. Look especially for a changed noun (users became customers) or a dropped qualifier (in this sample, among respondents).
5. Decide: keep as written, rewrite with the correct scope, or remove.

A draft with six statistics takes roughly an hour to verify this way. If that feels expensive, ask whether the article needs six statistics. Often two well-sourced figures carry an argument better than six unverified ones.

When the number is yours

Statistics from your own analytics deserve the same scrutiny, with one addition: the definition of the event. "Conversions" in one report might be form submissions; in another, qualified leads; in a third, paid accounts. When a draft compares your figure with an industry figure, the two definitions almost never match.

Publish the counts alongside the percentage whenever the sample is small, and say what the event was. A reader who sees "4 signups from 20 sessions" can judge the evidence; a reader who sees "20% conversion rate" cannot.

Put this into practice

Copy the worksheet columns below into a spreadsheet and keep one row per item you check. The filled row is an illustrative example, not a reported customer result; replace it with your own verified records.

Use the following prompt only after supplying the records it requests:

Audit these statistics against the supplied original sources. Return numerator, denominator, population, collection period, method, supported wording and unsupported inference. Do not supply a missing number from memory.

Research context

Statistics collections are research starting points; a publishable numerical claim still needs its original scope and denominator. The related Ahrefs starting points are 107 SEO Statistics for 2026 and 90+ AI SEO Statistics for 2025 (Fresh and Original Data). This guide’s checklist, examples and proposed workflow are independently written; they are not results of a SEOVision experiment.

Continue with the next task

• Build an Evidence Ledger for AI-Assisted Articles
• A Human–AI Content Workflow That Protects Quality and Trust
• AI Content Refresh: Update Evidence, Not Just the Date

Sources

• 107 SEO Statistics for 2026 — Research starting point; not an endorsement of this original workflow
• 90+ AI SEO Statistics for 2025 (Fresh and Original Data) — Research starting point; not an endorsement of this original workflow