One Stat Every 150 Words: The Density Trick Behind AI Citations

Why do answer engines prefer pages full of statistics?

A statistic is the cleanest thing a model can lift. "Conversion rose 18% after we added photo reviews" is a self-contained, falsifiable claim; "conversion improved a lot" is an opinion the model has no reason to repeat. Answer engines are built to return specific, checkable answers, so the passages they quote skew heavily toward sentences that carry a concrete number.

Density matters because extraction happens at the passage level, not the page level. A page that drops one figure in the intro and then coasts gives the model a single quotable unit. A page that carries a sourced number through every section gives the model a dozen, which raises the odds that one of them matches the exact question a buyer asked.

How many statistics should a page actually have?

The working heuristic is one concrete, cited number every 150 to 200 words. That is a target, not a quota: padding a page with weak figures to hit a count produces text no human wants to read and no model trusts.

Think of it as a rhythm rather than a rule. Each claim that could carry a number should carry one, and each number should carry its source. Where you genuinely do not have a figure, say so plainly rather than inventing one to keep the cadence.

• Lead each section with the claim, then support it with the figure, not the reverse.
• Prefer a specific number (18%, 13 weeks) over a rounded gesture ("most", "a majority").
• Attach the source to the sentence, not to a footnote three screens away.
• Cut a statistic that does not change what the reader would do next.

What counts as a statistic that gets quoted?

A quotable statistic is specific, attributed, and dated. Specific means a real figure rather than a vague intensifier. Attributed means a named source the model can weigh. Dated means the reader can see whether it is current, because a number from 2019 reads differently than one from this year.

The failure mode is the orphan number: a percentage with no source, no date, and no context. A model can extract it, but it has no reason to trust it, and a reader who checks finds nothing behind it. An orphan number looks like rigour and behaves like noise.

How do I source numbers honestly without fabricating precision?

Fabricated precision is the trap: writing "73.4%" when you have a rough sense, or inventing a study that does not exist to make a paragraph sound authoritative. It reads well until someone checks, and answer engines and their human raters increasingly do check. An invented statistic is a direct E-E-A-T risk, and the penalty is not just that one claim being dropped; it is the page losing the trust that gets anything cited.

Honest sourcing is slower and worth it. Use your own data where you have it and label it as yours. Cite third parties by name and date. When a number is an estimate, mark it as an estimate ("roughly", "about") rather than dressing it in false decimals. Conceding a soft figure is more credible than asserting a hard one you cannot defend.

• Quote your own measured data and label it as first-party.
• Name the third-party source and the year, every time.
• Round honestly: "about a third" beats a fake "33.4%".
• If you cannot source it, frame it as a claim, not a statistic.

How does statistics density connect to E-E-A-T?

E-E-A-T rewards demonstrated experience and trustworthiness, and a sourced number is the most direct evidence of both. A page that says "we measured this and here is the figure, from this source, on this date" shows experience the way a page of adjectives cannot. Density of honest, attributed statistics is, in practice, a density of trust signals.

The inverse is the warning. Fabricated or unsourced figures do the opposite: they signal that the author is reaching for authority they have not earned, which is precisely what E-E-A-T evaluation is designed to catch. The same trait that earns citations, verifiable specificity, is the trait that loses them when it is faked.

How do I add statistics to review and product content?

Most stores are sitting on first-party numbers they never surface. Aggregate rating, review count, the share of reviewers who mention a specific use case, the percentage who repurchased: these are real, defensible statistics a buyer cares about and an answer engine can quote. The work is getting them out of a dashboard and into readable text on the page.

This is where most review apps stop. They were built to render stars for the on-page shopper and leave the underlying numbers trapped in a widget, unreadable and unquotable. Getting your existing review data rendered, corroborated, and cited (in search and in AI answers) is the gap BetterReviews is built to close. The figures already exist in your reviews; the task is making them extractable and attributable rather than decorative.

What this adds up to

Statistics get cited because they are extractable and verifiable; they lose you citations the moment they are faked. Carry one honest, sourced number every 150 to 200 words, attach a name and a date to each, and round truthfully when you must estimate. Specificity is the asset, and honesty is what keeps the asset from turning into a liability.