Why You Shouldn’t Trust the First Result in Google (and How to Ask to Get a Good Answer)

01Why a position in the results doesn’t mean truth

Google doesn’t evaluate whether content is true — it evaluates its relevance, the site’s authority in the eyes of the algorithm, and the quality signals known by the acronym E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness). These are approximations of credibility, not a measurement of it. The algorithm can be manipulated, and it can also be fooled — and “authority” in the SEO sense (lots of links, good site structure) isn’t the same as subject-matter authority.

The problem is deepened by something researchers call trust bias (sometimes: position bias). A classic 2005 eye-tracking study by Thorsten Joachims and a team from Cornell showed that users trust the search engine’s ranking more than their own judgment of relevance — even when the results were deliberately swapped, participants still clicked more often on whatever was higher. Newer research confirms this pattern: the first click usually happens within about 6.5 seconds and almost always falls within the top three or four positions, regardless of whether they are actually the most relevant.

The phenomenon has intensified with AI Overviews. According to a Pew Research Center study from July 2025 (a sample of 900 adult users in the US), when an AI summary appears above the results, users click on source links less often — and only 1% of visits to pages with an AI Overview end with a click on a cited source. More and more people trust the ready-made answer at the top of the page and never reach the material that would confirm it.

On top of this comes ordinary SEO — an entire industry of “content farms” and “link farming” creating content solely for algorithms, often at the expense of reliability — and a more serious phenomenon: SEO poisoning. Cybercriminals deliberately rank fake websites (e.g., ones impersonating download pages for popular software) so that they appear high for phrases like “download [tool name].” In 2025, Zscaler and FortiGuard Labs documented several large campaigns of this kind distributing malware (Vidar, Lumma, BadIIS) precisely through poisoned search results. So the first result may not only lie — it may also infect your computer.

02Gallery of blunders: when Google gets it wrong

These aren’t isolated cases. Below are ten documented examples, repeatedly confirmed in independent sources — from featured snippets, through the knowledge panel, to the newest AI Overviews and search suggestions.

03AI Overviews — a new, bigger problem

In 2026, “The New York Times,” working with the analytics firm Oumi, tested 4,326 queries in Google using the SimpleQA benchmark. The older Gemini 2 model was wrong in about 15% of answers, the newer Gemini 3 — in about 9%. More interestingly: even when the answer was factually correct, in 56% of cases the linked source didn’t confirm it at all — that is, the AI gives a “fact” and next to it a link that doesn’t prove that fact. Google publicly challenged this analysis, accusing it of a flawed benchmark methodology and of not being representative of real user queries — it’s worth noting this rather than treating the number as indisputable.

At a scale of about 5 trillion searches a year, even “only” 9% wrong answers means tens of millions of false answers generated every hour. Publishers feel the effects too: according to a Define Media Group report (an analysis of 64 large newsrooms), after the full rollout of AI Overviews, search traffic to publishers fell by an average of 42% — most sharply for how-to and encyclopedic content, precisely where accuracy matters most.

It’s worth adding: this isn’t solely Google’s problem. Systems from other companies have had similar slip-ups — Apple’s artificial intelligence became a symbol of this when at one point it informed users about the supposed arrest of Israel’s prime minister. The mechanism is the same: large language models don’t check facts the way a human does — they predict the most likely sequence of words based on their training data. Sometimes the pattern matches the truth. Sometimes it doesn’t.

04How to ask Google to get a good answer

The other half of the same puzzle: the more precisely you formulate a query, the less you’re at the mercy of the algorithm. Short, noun-based phrases with keywords usually work better than full sentences — and search operators, though associated mainly with SEO, are a real tool for getting straight to primary sources.

Good practice: start with one operator, check the results, add another. If the number of results drops to zero — undo the element you added last. And remember the wording: research on confirmation bias in searching shows that the way a question is asked distorts the result in itself — search engines tend to return evidence confirming the wording of the query, not opposing evidence phrased in other words. If you ask “is X harmful,” you’re likely to get a different set of results than if you ask about “the effectiveness and safety of X.”

05The SIFT method and the 5×W rule

This is the concept most lacking in the conversation about searching. Mike Caulfield of the University of Washington developed the SIFTmethod, used reflexively by professional fact-checkers:

A key element of SIFT is so-called lateral reading: instead of judging a website’s credibility on the basis of the site itself, open a new tab and check what others say about the source. Research by the Stanford History Education Group showed that fact-checkers handle this task radically better and faster than history professors with PhDs — because the latter dig into the content of the page, while fact-checkers verify the source first.

06Checklist: before you believe the first result

07Frequently asked questions