Hoskin, Tanya. "Parametric and Nonparametric: Demystifying the Terms." Mayo Clinic CTSA BERD Resource. Retrieved from http://www. mayo. edu/mayo-edudocs/center-for-translational-science-activities-documents/berd-5-6. pdf(2012)
Question 4 from the second paper of 2004, which asked for details about parametric and non-parametric tests, was passed by 22% of the candidates, which is slightly better than Question 4 from the second paper of 2003 which asked the candidates to "compare and contrast the use of the Chi-squared test, Fisher’s Exact Test and logistic regression when analysing data". Such things seem to now be behind us.
You use these to figure out the p-value, i.e. the chance of getting the same results if the null hypothesis were true.
There are parametric and non-parametric tests.
parametric tests are more accurate, but require the assumption to be made about the data, eg. that the data is normally distributed.
Description of parametric tests
Parametric tests are more accurate, but require assumptions to be made about the data, eg. that the data is normally distributed (in a bell curve). If the data deviate strongly from the assumptions, the parametric test could lead to incorrect conclusions.
If the sample size is too small, parametric tests may lead to incorrect conclusions due to the loss of "normality" of sample distribution.
Examples of parametric tests:
Description of non-parametric tests
Non-parametric tests make no assumptions about the distribution of the data. If the assumptions for a parametric test are not met (eg. the distribution has a lot of skew in it), one may be able to use an analogous non-parametric tests.
Non-parametric tests are particularly good for small sample sizes (<30). However, non-parametric tests have less power.
Examples of non-parametric tests: