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Did you know that statistics can never prove that there is (or is not) a relationship between a pair of variables?
If that’s the case, then what is the point of statistics, I hear you ask...
Well, statistics is the study of uncertainty. If you’ve already proven beyond all doubt that a relationship exists between this and that, then there is nothing to be gained from a statistical analysis. It is only when there is uncertainty about the relationship that we can learn something by using stats. It is for this reason that statistics can neither prove nor disprove the existence of a relationship. It can only tell you how likely or unlikely that a relationship exists.
So what is a statistical relationship?
When you can phrase your hypothesis (question or hunch) like the following, then you are talking about the relationship family of statistical analyses:
Typically, the terms correlation, association and relationship are used interchangeably by researchers to mean the same thing. That’s absolutely fine, but when you talk to a statistician you need to listen carefully – when she says correlation, she is most probably talking about a statistical correlation test, such as a Pearson correlation.
There are distinct stats tests for correlations and for associations, but ultimately they are all testing for the likelihood of relationships in your data.
When you are looking for a relationship between two continuous variables, such as height and weight, then the test you use is called a correlation test. If one or both of the variables are categorical, such as smoking status (never, rarely, sometimes, often or very often) or lung cancer status (yes or no), then the test is called an association test.
Just a little reminder about the Resources page that accompanies this book.
If you want to learn more about correlations and associations (and get even more FREE stuff), this is where you’ll find it: