Post by account_disabled on Feb 22, 2024 5:34:35 GMT -5
The opportunities. In this post Im going to outline data analysis pitfalls that are endemic in our industry and how to avoid them. . Jumping to conclusions Earlier this year I conducted a ranking factor study around brand awareness and I posted this caveat ...the fact that Domain Authority or branded search volume or anything else is positively correlated with rankings could indicate that any or all of the following is likely Links cause sites to rank well Ranking well causes sites to get links Some third factor e.g. reputation or age of site causes sites to get both links and rankings Me However.
I want to go into this in a bit more depth and give you a America Mobile Number List framework for analyzing these yourself because it still comes up a lot. Take for example this recent study by Stone Temple which you may have seen in the Moz Top or Rands tweets or this excellent article discussing SEMRushs recent direct traffic findings. To be absolutely clear Im not criticizing either of the studies but I do want to draw attention to how we might interpret them. Firstly we do tend to suffer a little confirmation bias were all too eager to call out the clich correlation vs. causation distinction when we see successful sites that are keywordstuffed but all too approving when we see studies doing the same with something we think is or was effective like links.
Secondly we fail to critically analyze the potential mechanisms. The options arent just causation or coincidence. Before you jump to a conclusion based on a correlation youre obliged to consider various possibilities Complete coincidence Reverse causation Joint causation Linearity Broad applicability If those dont make any sense then thats fair enough theyre jargon. Lets go through an example Before I warn you not to eat cheese because you may die in your bedsheets Im obliged to check that it isnt any of the following Complete coincidence Is it possible that so many datasets were compared that some.
I want to go into this in a bit more depth and give you a America Mobile Number List framework for analyzing these yourself because it still comes up a lot. Take for example this recent study by Stone Temple which you may have seen in the Moz Top or Rands tweets or this excellent article discussing SEMRushs recent direct traffic findings. To be absolutely clear Im not criticizing either of the studies but I do want to draw attention to how we might interpret them. Firstly we do tend to suffer a little confirmation bias were all too eager to call out the clich correlation vs. causation distinction when we see successful sites that are keywordstuffed but all too approving when we see studies doing the same with something we think is or was effective like links.
Secondly we fail to critically analyze the potential mechanisms. The options arent just causation or coincidence. Before you jump to a conclusion based on a correlation youre obliged to consider various possibilities Complete coincidence Reverse causation Joint causation Linearity Broad applicability If those dont make any sense then thats fair enough theyre jargon. Lets go through an example Before I warn you not to eat cheese because you may die in your bedsheets Im obliged to check that it isnt any of the following Complete coincidence Is it possible that so many datasets were compared that some.