This is an example of what a typical search result would look like. Position 3 may be the result of How to Do A. Position 4 may be about where to buy A. Position 5 may be about a review of A and its competitors B and C. Position 6 may be about the latest news about A. In the above example, each site is ranked, not due to the amount of links. They are ranked according to the most popular search intent. It's like the search results for the search term Jaguar. The top result (car site) isn't there because
It has more backlinks than the Animal ghost mannequin effect Jaguar Wikipedia page. The most popular search purpose for the phrase Jaguar is a web page about Jaguar cars, so you'll see the top results. The number of backlinks between positions 1, 3, 4, 5, and 6 has nothing to do with why these pages are ranked in these positions. A particular search query usually has multiple search intents. advertisement Continue reading below Therefore, correlation studies that draw conclusions from the top 10 or 20 of Google's search results can provide information that does not accurately reflect how
Google ranks web pages. To get more accurate results, research studies must first identify different intents and assign them classifications such as information, transactions, and education. But even if that is done, it is still flawed. The search intent classification does not match the search intent that Google used to create a particular SERP. Related: How to find people: Understand the user's intentions Engineer search results cannot be reverse engineered Unraveling Google search results through correlation studies is not as easy as correlating ranking factors with millions of SERPs for the reasons mentioned above. Correlation studies have always been unreliable. Still, many people continue to believe in them. They make great clickbait. But perhaps it's time for the SEO industry to grow and put them aside.