The Great Debate Correlation vs Causation

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1 The Great Debate Correlation vs Causation

2 Simply Put.. Correlation is: A mutual relation between two or more things where one has a measurable effect on the other. Causation is: A relationship in which one action or event is the direct consequence of another.

3 Note in every CAUSATION there is a CORRELATION BUT NOT every CORRELATION EQUALS CAUSATION

4 So if I you were hit a ball with a Tennis Racket the act of hitting the ball and how far the ball goes is DIRECTLY MEASURABLE and CAUSAL we even have formulas for it. f=ma f=mv/t or f=p/t Only casuals have directly measurable effects.

5 Often Correlation is MISTAKEN for Causation! A is PERCEIVED to CAUSE B By the observer - When in actuality it may or may NOT Have an measurable effect.

6 Observer Perception is often incorrect

7 Visual Example How perception can perceive change that does not exist

8 We could make assumptions based on old ideas or incorrect perception. For example, site code on rankings pre vs post Google s PANDA or PENGUIN updates.

9 We could make assumptions based on old ideas or incorrect perception. For example, site code on rankings pre vs post Google s PANDA or PENGUIN updates. TWO updates were released about the same time, so some assumed their sites were affected by Penguin when they were ACTUALLY affected by Panda.

10 No Correlation Where we think there is one Or a Spurious Correlation Were we believe A effects B When actually there is a hidden third variable So What is NO Correlation? What is a Spurious Correlation?

11 The perception that A effects B but in actuality a third hidden variable does.

12 So the sites who thought they were affected by this But were actually were affected by this

13 The perception that A effects B, but in actuality there is NO RELATIONSHIP. So if my sites dropped out of the index tomorrow I might ASSUME it is because of this

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15 The perception that A effects B, but in actuality there is NO RELATIONSHIP. I would make assumptions based on incorrect perception.

16 In causal relationships, you can directly measure that A does CAUSE B In correlative relationships, you can only measure the STRENGTH of the effect of A on B

17 Causal Relationships If you out heat under water for X time, water will boil. Heat CAUSES Water to boil at 212*. Correlative SEO Example: We know that if you use too much exact match keyword text in your links and your site drops in rankings, you can go remove those keywords as the most likely source of the issue. HOWEVER, this is a correlative relationship. It cannot be proven (unless Matt or GOOGLE tells you), only measured as an effect of the relationship of variables >> Exact match text and Site ranking drop

18 Why? Because you also have to know What are your variable types Random, Snowball etc What is your sample size? Is it large enough to normalize your data? Do you need to? Are you controlling for outliers? It only takes a few outliers to completely alter your results and make them worthless for extrapolation. Are you choosing the proper analysis method? Variable selection helps determine this, however do you need linear regression to help control for variable interactions? Or can you extrapolate your data without extra measures? If you are not controlling for these, are you able to understand the limitations of your conclusions? Have you chosen the proper methodology? Etcetera etcetera

19 SEO is almost ALWAYS correlative. We can measure the effect of variable A on Variable B, the environment dictates that there are a multitude of variables that are either possibly unknown, or their effect is unknown. This means we have an inability to directly measure variable effect. I.E. Since these variable effects are unknown, it is very difficult to state that A CAUSES B, we can only say we believe that to change or alter a factor of A effects a Y change in B, but that this is a visible relationship that can be statistically examined, not proven. This is not true of ALL SEO factors, but for most, which is why two SEOs might come to two very different conclusions on the same data.

20 TEST TEST TEST TEST TEST TEST TEST and TEST again!! If you want to become an authority on how things work suggest using correlative testing measures such as regression, linear regression, chi squares etc. These measure the strength of correlative relationships as well as the validity of your variables. Then once you test. Test again. You can NEVER say the relationship is CAUSAL when it is CORRELATIVE, but you can be certain within X%.

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