Correlation is one of the most often used (and most often misused) kinds of descriptive statistics.  It is perhaps best described as “a single number that describes the degree of relationship between two variables.” If two variables tend to be “correlated,” it means that a participant’s score on one tends to vary with a score on the other.  For example, people’s height and shoe size tend to be positively correlated.  This means that for the most part, if a given man is tall, he is likely to have a large shoe size.  If short, he is likely to have a smaller shoe size.  Correlation can also be negative. For example, the temperature outside in Fahrenheit may be negatively correlated with the number of hot chocolates sold at a local coffee shop.  This is to say that as the temperature goes down, hot chocolate sales tend to go up.  Although causality may seem to be implied in this situation, it is important to note that on a statistical level, correlation does not imply causation.  A good researcher knows that there is no way to assess from correlation alone that a causal relationship exists between two variables. In order to assert that “X caused Y”, a study should be experimental, with control groups and random sampling procedures.  Determining causation is a difficult thing to do, and it is a common mistake to assert a cause-and-effect relationship when the study methodology does not support this assertion.


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