12. Write short notes on when you would use parametric and 
       non-parametric statistical tests.
Parametric Tests
They test hypotheses about specific parameters of the population such as the mean or the variance. 

They  make the following assumptions:

  • The scores must be independent ie. the selection of any particular score must not bias the chance of any other case for inclusion.  
  • The observations must be drawn from normally distributed populations.

  • The populations (if comparing two or more

  • groups) must have the same variance.
  • The variables must have been measured in at least an interval scale so that it is possible to interpret the results.
Example of such test is the two sample t-tests
Non-Parametric Tests
Non-parametric tests on the other hand are based on a statistical model that has only very few assumptions.

None of these assumptions include making assumptions about the form of the population distribution from which the sample was taken.

Whenever categorical or ordinal data are looked at, we should use non-parametric tests.  

Furthermore, if the data can be shown to be not normally distributed non-parametric tests should be used.

Example of such test is Wilcoxon Mann-Whitney Test 

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