Statistical Evidence

Element Code: CO-024

What this check is looking for

Statistical Evidence flags content that asserts a lot but proves little. "Most companies struggle with this" is an opinion dressed as a fact. "In a 2024 survey of 500 marketers, 63 percent reported X, according to Source Y" is evidence. This check exists to nudge you from the first kind of sentence toward the second.

The point is not to bury readers in numbers. It is to back the claims that carry your argument with concrete, attributed data so a skeptical reader and a citation-hungry AI system both have something solid to hold onto.

Why statistics move the needle for GEO

Two audiences reward evidence. Human readers trust a page more when its claims come with numbers and sources, because it signals the author did homework rather than winging it. That trust shows up as longer sessions, more links, and better conversions.

The second audience is AI answer engines, and this is where it gets interesting. Systems like ChatGPT, Perplexity, and Google AI Overviews are built to give users specific, defensible answers. A precise, attributed statistic is exactly the kind of atomic fact they can lift and cite with confidence. When your page is the cleanest source of a specific number, you become the passage the model quotes. A vague generality gives the model nothing to grab, so it moves on to a competitor who did the sourcing.

There is a real risk here worth naming: do not fabricate statistics to chase this benefit. An invented number that gets picked up and repeated is a credibility landmine, and it can blow up on you when someone checks the source and finds nothing. Accuracy is not optional. A well-sourced smaller set of numbers beats a wall of impressive-sounding figures you cannot stand behind.

Claim to citable fact

How to detect thin evidence

1. Highlight every claim. Read the page and mark each sentence that asserts something as true about the world. For each, ask: is there a number and a source, or is this just an assertion?
2. Search for weasel words. Grep your content for "most", "many", "studies show", "experts agree", "the majority". These almost always mark a spot where a real statistic should live.
3. Check the source density. A page making strong claims with zero outbound citations to data, research, or primary sources is a red flag for this check.
4. Test citability directly. Ask an AI system a factual question your page addresses. If it cites a competitor's specific number instead of your vague sentence, you just watched this problem cost you a citation.
5. Verify what you already cite. Old statistics rot. A figure that was current three years ago may now be wrong. Recheck every number against its source and confirm the source still says what you claim.

How to fix it, step by step

1. Find your load-bearing claims. Not every sentence needs a statistic. Identify the two or three claims your argument actually rests on and prioritize evidence for those.
2. Source real data. Pull from primary research, government datasets, reputable industry reports, or your own first-party analytics. First-party data is gold here because no competitor has it.
3. Attribute inline. Name the source and the year in the sentence itself: "according to Source, 2024". Do not hide attribution in a footnote a model will never associate with the number.
4. Link to the primary source. Link to the original study or dataset, not to a blog that cites a blog that cites the study. Chains of secondhand citation are where numbers mutate.
5. Present numbers cleanly. A short data table or a single clear sentence beats a number buried mid-paragraph. Make the figure easy to extract.
6. Date everything and schedule a recheck. Put the year on each statistic and set a reminder to re-verify sources on a cadence, so your page does not quietly go stale.

What good looks like

FAQ