Evidence Density Score

Extended definition

Evidence Density Score quantifies how much citeable, factual information exists per paragraph or section of content. High-density content packs statistics, research citations, data points, specific examples, and verifiable claims into concentrated blocks. Low-density content has opinions, generalizations, and fluff that AI engines skip over when looking for supporting evidence. The score helps predict citation likelihood: dense content provides more extraction opportunities. Measurement looks at facts per 100 words, citations per section, and specific vs. vague claim ratios.

Why this matters for AI search visibility

AI engines prioritize fact-dense sources because they need evidence to support generated answers and reduce hallucination risk. Content with high Evidence Density appears more authoritative and gets cited more frequently because it provides the specific, verifiable details AI systems are trained to extract. For B2B content marketing, evidence density separates thought leadership that gets cited from opinion pieces that get ignored. When AI engines decide between two sources covering the same topic, higher density usually wins the citation.

Practical examples

• Content with 12 data points per 100 words sees 4.1x higher citation rate than content with 2 data points per 100 words
• Rewriting fluffy blog post to include specific statistics and research citations increases AI extraction rate from 8% to 67%
• A/B test shows adding concrete examples with numbers improves answer inclusion rate from 23% to 71%