What does the statistics check look for?
It measures how much of your content is grounded in concrete data versus vague assertion. It looks for:
- Specific figures — numbers, percentages, dates, prices and measurements rather than "many", "recently", "a lot".
- Verifiability — facts precise enough to be checked and, ideally, attributed to a source.
- Density — whether data appears throughout the content or it's mostly generic prose.
Content rich in specific, verifiable figures passes; some data amid mostly generic prose is a warning; no specific figures is a fail.
How is it evaluated, and how is it scored?
GEObubbly counts paragraphs that carry concrete numbers, percentages, dates or measurements against those making only vague claims. It's a core GEO / LLM Readiness check worth 3 points, because specificity is one of the clearest predictors of whether a passage is quotable enough for an engine to cite.
Why statistics and specificity matter for GEO
AI engines prefer to cite specific, verifiable facts over vague claims, because a precise statement is exactly what an answer can be built on. "Conversions rose 30% in Q1 2026" is quotable; "conversions improved significantly" is not. Studies of AI citations have found that content rich in statistics, data and concrete figures earns substantially higher visibility in generative answers. The practical move is to audit your content for vague assertions that could be made specific: replace "many users" with a number, "recently" with a date, "a lot faster" with a measurement, and cite the source of the figure where you can — that adds the credibility engines look for. This isn't about stuffing numbers in for their own sake; it's about making your genuine claims precise and verifiable, which is exactly what makes a passage worth quoting. It works alongside sourced claims and self-contained passages across GEO / LLM Readiness.