Correlation ≠ Causation
Just cause there is an assosciation between two variables does not mean that one variable causes the other.
Icecream + Murders
There is a Correlation Coefficient of R = 0.82 between two variables, Icecream Sold and Murder rate, does not mean selling less icecream will result in less Murders. The Underlying cause is Temperature as it affects both of them
Confouding Variable
The Confounding variable is the third factor that is impacting both variables. in the Icecream + Murders example this is Temperature, these to things are coincidental but are still not related, because Correlation does not equal Causation.
Association
When Interpreting a dataset we can only comment on what we know, e.g. A Strong Negative Relationship between Body Fat and Excercise rates can not determine that Excercise is the best way to reduce Body Fat, as Correlation ≠ Causation, and there could be another factor at play, e.t.c Diet
title: Model Answer
collapse: close
Q: A survey of a large number of cities revealed a high correlation between the number of police offices and the nuber of crimes commited.
Does this mean that more police Officers are causing more crimes to be commited? Dicuss.
<br>
--------
<br>
--------
<br>
A:
It does not make any snese to conclude that the number of olice officers would be causing more crimes to be commited,Cities with high crime rates would actually need higher numbers ofp olice officers. It is more than likely that both of these variables increase in increase in cities with higher populaitons, Lower Economic Status, and other possible [[#confouding-variable|Lurking (Confounding)]] variables
References