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Statistical techniques are used to explore connections between independent and dependent variables.  This connection between or among variables is often referred to as association.  Association is also known as covariation

and can be defined as measurable changes in one variable that occur concurrently with changes in another variable.  A positive association is represented by change in the same direction (income rises with education level).  Negative association is represented by concurrent change in opposite directions (hours spent exercising and % body fat).  Spurious associations are associations between two variables that can be better explained by a third variable.  As an example, if after taking cold medication for seven days the symptoms disappear, one might assume the medication cured the illness.  Most of us, however, would probably agree that the change experienced in cold symptoms are probably better explained by the passage of time rather than pharmacological effect (i.e., the cold would resolve itself in seven days irregardless of whether the medication was taken or not).


There is a difference between determining association and causation.  Causation, often referred to as a relationship, cannot be proven with statistics.  Statistical techniques provide evidence that a relationship exists through the use of significance testing and strength of association metrics.  However, this evidence must be bolstered by an intellectual exercise that includes the theoretical basis of the research and logical assertion.  The following presents the elements necessary for claiming causation:

Required elements for causation

Association                              Do the variables covary?

Precedence                             Does the independent variable vary before the affect exhibited in the dependent variable?

Plausibility                                Is the expected outcome consistent with theory/prior knowledge?

Nonspuriousness                     Are no other variables more important?



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