<?xml version="1.0" encoding="UTF-8"?><rss version="2.0" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:sy="http://purl.org/rss/1.0/modules/syndication/" > <channel><title>Comments on: Correlation Equals Causation</title> <atom:link href="http://dejanseo.com.au/correlation-equals-causation/feed/" rel="self" type="application/rss+xml" /><link>http://dejanseo.com.au/correlation-equals-causation/</link> <description></description> <lastBuildDate>Tue, 18 Jun 2013 09:08:00 +0000</lastBuildDate> <sy:updatePeriod>hourly</sy:updatePeriod> <sy:updateFrequency>1</sy:updateFrequency> <item><title>By: Andy Langton</title><link>http://dejanseo.com.au/correlation-equals-causation/#comment-747</link> <dc:creator>Andy Langton</dc:creator> <pubDate>Fri, 15 Jun 2012 08:49:00 +0000</pubDate> <guid isPermaLink="false">http://dejanseo.com.au/?p=7797#comment-747</guid> <description>The point of reinforcing &#039;correlation does not imply causation&#039; (note: imply!) is nothing to do with fear or casting blanket doubt, but everything to do with an element of scientific rigour. You need a reason to believe that a relationship is causal, otherwise you are best to assume that it isn&#039;t. This step back from a dataset is a crucial part of interpreting findings and turning those into useful actions.To my mind, the SEO community in general needs a healthy dose of learning how to interpret and analyse data reliably - not the encouragement not to bother.No-one can decry the findings of an analysis on the basis of &#039;correlation not implying causation&#039; if you have adequately accounted for causation in the first place! </description> <content:encoded><![CDATA[<p>The point of reinforcing &#8216;correlation does not imply causation&#8217; (note: imply!) is nothing to do with fear or casting blanket doubt, but everything to do with an element of scientific rigour. You need a reason to believe that a relationship is causal, otherwise you are best to assume that it isn&#8217;t. This step back from a dataset is a crucial part of interpreting findings and turning those into useful actions.</p><p>To my mind, the SEO community in general needs a healthy dose of learning how to interpret and analyse data reliably &#8211; not the encouragement not to bother.</p><p>No-one can decry the findings of an analysis on the basis of &#8216;correlation not implying causation&#8217; if you have adequately accounted for causation in the first place! </p> ]]></content:encoded> </item> </channel> </rss>
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