Statistics I:  Interpreting Results


In one sense, the meaning of the tests is given in their definitions.  Descriptive statistics describe characteristics of the distribution of data.  Inductive statistics allow you to draw conclusions about the relationship between variables:  whether there is one, how strong it is, and sometimes even what it looks like.

 

In another sense, you have been told nothing about these statistics.  Each of these tools has been developed in a context of statistical theory, very little of which has been transmitted here.  This unit is no substitute for a course in statistics.  To really understand parametric statistics, you should study the “normal curve” and its characteristics.  To really understand significance testing, you should study probability theory. 

 

There is another sense in which the discussion here is lacking:  I have presented the statistics in their basic form.  Almost all of them have corrections which must be applied in special circumstances.  Chi-square, in the form I have given you, requires a minimum value of 10 for the expected frequency in each cell.  There are adjustments to the formula which will allow you to tolerate as few as 5 expected observations per cell; under no circumstances will Chi-square work if an expected cell frequency is 0.  Adjustments like these are the topics of courses in statistics.

 

The purpose of these two units on statistics is to provide you with a basic framework for doing statistical analysis.  If you have already taken a course in statistics, you can make the adjustments in the formulas in the template as circumstances dictate.  If you have not studied statistics yet, there are several good references listed in the bibliography.

 

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© 1996 A.J.Filipovitch
Revised 11 March 2005