I don't typically like to argue on a third party site, but the post in question sadly doesn't allow comments. The original post, however, does - http://www.seom

I don't typically like to argue on a third party site, but the post in question sadly doesn't allow comments. The original post, however, does - http://www.seomoz.org/blog/google-vs-bing-correlation-analysis-of-ranking-elements http://www.seomoz.org/blog/google-vs-bing-correlation-analys... - and I wish Ted had taken up this discussion there so as to permit/invite feedback. The research itself was presented in, what I (and many, many others) believe to be precisely the right fashion. We pulled page 1 results from 11,000+ search results, then looked at Spearman's correlation co-efficient for each element. This blogger pulled out a small section of the post and disagreed with the recommendations we made, which I think is perfectly fine and reasonable. However, to discount/discourage future research or suggest an entire field is illegitimate seems inappropriate. It would seem, for example, highly subjective to include the bit he did without also including this portion: Methodology - We collected 11,351 search results from both Google & Bing via Google AdWords suggest data for the various categories (you can see these keywords yourself via Google's AdWords tool) - We looked only at the first page of results (which typically included 10 results, but sometimes contained a higher or lower number). We ignored all no-standard results (meaning universal or vertical results such as video, images, local or "instant answers") - The correlations relate to higher/lower positional ranking on page 1 of the search results - We controlled for search results where all (or none) of the results matched the metric. Thus, for example, if we were looking for correlation with .gov domains and no results in the set included a .gov domain, we didn't use that SERP for that dataset. - We've used Spearman's correlation coefficient, as it is the standard (and in our opinion, best choice) for ranked datasets. You can read more about this selection via Ben's comments here and here. and this: Understanding Correlation Significance The correlation numbers we show range between -0.2 and 0.35, where a perfect correlation would be 1.0 and no correlation would be 0.0. The standard error for each result set is also included, but tends to be so low in most cases that displaying it on the bar graph would make it nearly invisible. This is thanks to the large number of results collected - we've got very high confidence in the statistical significance of these. Correlation ≠ Causation It's long been held in statistical analysis that even very high correlations do not necessarily mean one data set is the cause of the other. People holding umbrellas don't cause rain. Ice cream sales don't cause hot weather. For example, the more I wear suits, the more I speak on panels about SEO. Does it therefore follow that wearing suits gets me onto panels about SEO? It's critical to know that the data below, like data from other types of SEO tests, requires careful consideration and analysis. Parsing a bigger correlation as a direct sign that one should do X or Y more would be a fallacy. I guess I'm just a little frustrated because this topic has been discussed over and over again, yet old stereotypes and ad hominem attacks still pervade. See previous pieces: http://news.ycombinator.com/item?id=1402544 http://news.ycombinator.com/item?id=1402544 http://searchengineland.com/an-open-letter-to-derek-powazek-on-the-value-of-seo-27680 http://searchengineland.com/an-open-letter-to-derek-powazek-... http://searchengineland.com/from-my-inbox-more-defense-of-seo-11189 http://searchengineland.com/from-my-inbox-more-defense-of-se... http://searchengineland.com/defending-seo-yet-again-10163 http://searchengineland.com/defending-seo-yet-again-10163 http://ycombinator.posterous.com/the-first-yc-conference http://ycombinator.posterous.com/the-first-yc-conference