Correlation: Difference between revisions

From The Jolly Contrarian
Jump to navigation Jump to search
No edit summary
No edit summary
Line 1: Line 1:
{{a|glossary|}}The idea, first articulated by statistician Karl Pearson<ref>So [https://slate.com/technology/2012/10/correlation-does-not-imply-causation-how-the-internet-fell-in-love-with-a-stats-class-cliche.html Slate Magazine argues, at any rate.</ref>, that a relationship between two variables could be characterised according to its strength and expressed in numbers.  
{{a|glossary|}}The idea, first articulated by statistician Karl Pearson<ref>So [https://slate.com/technology/2012/10/correlation-does-not-imply-causation-how-the-internet-fell-in-love-with-a-stats-class-cliche.html Slate Magazine argues, at any rate.</ref>, that a relationship between two variables could be characterised according to its strength and expressed in numbers.  


Now it is true that [[Correlation doesn’t imply causation]], but it doesn’t rule it out either. [[All other things being equal]], a [[correlation]] is more likely to evidence a [[causation]] than a ''lack'' of correlation, right? This is one of those logical canards, as Monty Python put it, “[[universal affirmative]]s can only be partially converted: all of Alma Cogan is dead, but only some of the class of dead people are Alma Cogan.”
Now it is true that [[Correlation doesn’t imply causation]], but it doesn’t rule it out either. And it is certainly true that a ''lack of correlation ''does'' imply a ''lack'' of causation.
 
[[All other things being equal]], a [[correlation]] is more likely to evidence a [[causation]] than a ''lack'' of correlation, right? This is one of those logical canards, as Monty Python put it, “[[universal affirmative]]s can only be partially converted: all of Alma Cogan is dead, but only some of the class of dead people are Alma Cogan.”





Revision as of 10:20, 23 September 2019

The Jolly Contrarian’s Glossary
The snippy guide to financial services lingo.™
Index — Click the ᐅ to expand:
Tell me more
Sign up for our newsletter — or just get in touch: for ½ a weekly 🍺 you get to consult JC. Ask about it here.

The idea, first articulated by statistician Karl Pearson[1], that a relationship between two variables could be characterised according to its strength and expressed in numbers.

Now it is true that Correlation doesn’t imply causation, but it doesn’t rule it out either. And it is certainly true that a lack of correlation does imply a lack of causation.

All other things being equal, a correlation is more likely to evidence a causation than a lack of correlation, right? This is one of those logical canards, as Monty Python put it, “universal affirmatives can only be partially converted: all of Alma Cogan is dead, but only some of the class of dead people are Alma Cogan.”


See also

References