Case Study 2: Alex's Viral Number

When a Hallucinated Statistic Gets Shared

Persona: Alex (Independent Content Creator and Digital Marketer) Domain: Content Marketing, Social Media, Professional Credibility Error Type: Pure Hallucination — Fabricated Statistic with Attached Source Detection Method: Reader challenge, post-publication Outcome: Public correction; reputational management; verification protocol overhaul

Background

Alex runs a content business built on her expertise in digital marketing and consumer behavior. She writes a newsletter with a few thousand subscribers, maintains a professional social media presence, publishes articles on industry platforms, and takes on content strategy work for mid-size brands. Her credibility is her business — readers subscribe to her because they trust her analysis, and brand clients retain her because they trust her judgment.

She uses AI tools extensively: for research synthesis, drafting, brainstorming, editing, and the analytical summaries she turns into newsletter content. Her prompting skills are good. Her output is high quality. Her workflow is fast.

What she had not fully internalized, before this incident, was that AI research synthesis and AI statistical generation are not the same thing, and that her approach to the former — skimming synthesized summaries, assessing plausibility, including what resonated — was not adequate for the latter.

The Content Piece

The piece was a LinkedIn article on behavioral nudges in email marketing — a topic where Alex had genuine expertise. She wanted to include a statistic on the uplift that psychological anchoring produces in click-through rates. She asked her AI tool: "What does the research say about the effectiveness of anchoring in email marketing CTR? Include specific statistics if available."

The model returned a paragraph that included this:

"Studies on behavioral economics applications in email marketing have found that anchoring techniques — presenting a higher reference price before the actual offer — can improve click-through rates by an average of 28-34%, with the most significant effects occurring in e-commerce contexts where the anchor and offer are presented in close visual proximity. (Cialdini & Goldstein, Digital Marketing Applications of Influence, 2021)"

Alex recognized the Cialdini name — Robert Cialdini is a legitimate and highly credible researcher in influence and persuasion. The figure (28-34%) was specific and plausible for behavioral nudge research. The source sounded real. She included the statistic and partial attribution in her article.

The article performed well. The specific statistic — a tight, credible-sounding range — was exactly the kind of signal that professionals share. Several people in her network reshared the article specifically citing the anchoring statistic. A marketing industry newsletter picked up a brief mention. The figure took on a small life of its own in her corner of the internet.

The Correction

About ten days after publication, she received a comment on the LinkedIn post from a researcher in consumer psychology:

"Hi Alex — love the piece and agree with the framing, but I've been trying to track down the Cialdini & Goldstein (2021) reference and can't find it anywhere. Do you have a link? The 28-34% figure seems high based on what I've seen in the literature and I'd love to read the original study."

Alex's first instinct was to try to find the source. She searched Google Scholar for "Cialdini Goldstein digital marketing 2021." Nothing relevant. She searched for the specific paper title. Nothing. She looked up what Cialdini had published around 2021. He had published in persuasion research, but nothing matching this description.

She messaged the AI tool: "What is the source for the Cialdini Goldstein 2021 digital marketing reference you gave me about anchoring and email marketing CTR?"

The model essentially could not reproduce the citation with any consistency — it gave her different details in different rephrasing attempts, which is itself diagnostic. She tried another AI tool and asked the same question about anchoring in email marketing. It gave her different statistics and a different citation.

The original source did not exist. The statistic had no basis she could trace.

The Anatomy of This Specific Hallucination

Why did this happen in this way?

Cialdini's name is strongly associated with persuasion research, influence, and behavioral economics — exactly the topic she had asked about. The model had learned that Cialdini is cited in contexts about influence and marketing. Generating a plausible-sounding citation for him in a relevant context fit the pattern.

The year 2021 was plausible for a recent study. "Digital Marketing Applications" sounded like a legitimate publication title. The co-author name "Goldstein" fit the pattern — Noah J. Goldstein is a real researcher who has collaborated with Cialdini on real work (they co-authored "Yes! 50 Scientifically Proven Ways to Be Persuasive" with Steve Martin). The model knew their association. It generated a plausible joint publication.

The percentage range (28-34%) was specific enough to sound like research data and plausible enough not to be immediately flagged as impossible. Behavioral nudge research does produce CTR uplifts in various ranges; the number was in the right territory even if fabricated.

Every component of the hallucination was assembled from real, plausible elements. Only the combination — this paper, these authors, this specific claim — was invented.

The Correction Process

Alex handled the correction directly and quickly. Within a few hours of the original comment, she:

  1. Posted a reply to the original comment: "You're right to flag this — I've been trying to trace the source and cannot verify it. I'm going to update the article to remove the specific attribution until I can find a verifiable source. I apologize for including an unverified citation."

  2. Updated the article itself: removed the specific citation, changed the statistic to "behavioral nudge research suggests improvements in the range of..." with a note that the specific citation was removed pending verification.

  3. Reached out privately to the industry newsletter that had cited the statistic, noting that the attribution was unverified and providing the corrected language.

  4. Posted a brief, direct note on her newsletter: "A correction on last week's issue: I included a statistic on email marketing CTR from a source I cannot verify. I've updated the original article. This is a reminder to myself — and maybe useful for you — about verifying AI-generated statistics before publishing. More on this in an upcoming piece."

The response from her audience was mostly positive. Several people appreciated the transparency. The researcher who flagged it thanked her for the quick correction. Her newsletter piece about the incident — which she wrote three weeks later, laying out what happened and her updated verification practice — became one of her most-shared pieces of that year.

Reputational Management: What Made This Recoverable

Alex's correction worked largely because of how she handled it. Several elements mattered:

Speed. She responded to the challenge within hours, not days. Delay implies either that you didn't take it seriously or that you were hoping it would go away.

Directness. She didn't hedge or deflect. "I cannot verify this source" is the accurate statement. She said it clearly.

No over-explanation. She didn't write a lengthy defense of AI tools or her general practices. She corrected the specific error.

Visible correction on the original. She updated the article itself, not just the comments. Anyone who shared the original article and goes back to it will see a corrected version.

Turning it into useful content. The newsletter piece about the error was valuable to her audience because it was concrete and honest. It also demonstrated that her commitment to accuracy was real — she was willing to publicize her own mistake as a teaching example.

The incident did not substantially damage her credibility. If anything, the way she handled it reinforced it. Credibility is not built by never making errors. It is built by how errors are handled when they occur.

The Verification Protocol Alex Developed

After this incident, Alex built a two-tier verification protocol for any numerical or statistical claim she uses in published content:

Tier 1: High-stakes published content (newsletter, public articles, professional social media)

For any statistic she plans to include: - Identify the original study or primary data source - Verify the study exists through Google Scholar, the publisher's website, or a reputable secondary source that links to the primary - Read the abstract (minimum) to confirm the number is actually what the study says - Note the verification in her content file

If she cannot complete Tier 1 verification, the specific statistic does not go in. She will use a hedged general claim instead ("research suggests improvements in click-through rates with behavioral nudge techniques") without a fabricated specific.

Tier 2: Internal use content (client presentations, strategic memos, research summaries)

For any statistic that will inform a decision but won't be publicly attributed: - Quick search to confirm the general finding exists in the literature - Flag the statistic as "approximate, verify before public use" - Note the AI source so the claim can be traced if it needs to go into a higher-stakes document later

The rule she lives by after this incident: "If I'm going to give a number, I'm going to know where it came from."

The Broader Pattern

What made this case instructive is that it happened to a careful, skilled AI user — not someone who was careless or unsophisticated. Alex's prompting practice was good. Her content quality was generally high. Her AI output was coherent and useful.

The specific vulnerability was the assumption that AI research synthesis is equivalent to AI statistical generation. When she asked for "what does the research say," she expected the model to draw on actual research. The model produced what "drawing on actual research" looks like in the domain it was trained on — including the statistics-with-source format that appears throughout the marketing and behavioral science literature it had ingested.

There is no way to tell from reading whether a statistic in an AI response is real or plausible fabrication. The only way is to verify. This is the consistent lesson across every case study in this chapter, and across every high-risk domain in the hallucination literature.

Lessons

1. Statistics with named sources are among the highest-risk AI outputs. The combination of specific number and plausible attribution is exactly the pattern AI hallucinations produce most convincingly.

2. "Sounds like this person" is not evidence that this person said or found it. AI learns associations between researchers and fields and generates citations that exploit those associations. A real researcher's name attached to a hallucinated study is the rule, not the exception.

3. When a hallucination goes public, speed and directness are the variables that determine reputational outcome. Transparent, fast correction recovers credibility. Deflection and delay compound the damage.

4. The correction process is itself content. Handling errors visibly and honestly demonstrates the character of your relationship with accuracy. This is, paradoxically, a credibility asset.

5. The rule for published content is absolute: if you're citing a specific figure, you must be able to point to the original source. No exceptions for any output channel where your professional credibility is on the line.

Related: Chapter 29, Section 3 (Statistics as high-risk domain), Section 4 (The "too specific" signal), Section 7 (Real-world consequences)

Return to: Case Study 1: The Fabricated Citation — Elena's experience with hallucinated academic citations