1. What are the most common reasons you would select a non-parametric test over the parametric alternative?
2. Discuss the issue of statistical power in non-parametric tests (as compared to their parametric counterparts). Which type tends to be more powerful? Why?
3. For each of the following parametric tests, identify the appropriate non-parametric counterpart:
a. Dependent t-test
b. Independent samples t-test
c. Repeated measures ANOVA (one-variable)
d. One-way ANOVA (independent)
e. Pearson Correlation
(1) If a statistical variable understudy can be represented on a ratio or at least an interval scale of measurement, then it is said to have proper units. With some assumption made about the distribution of the variable (say normality, for example), the variable is fit to be tested parametrically. The t-test, the z-test etc are a few well-known parametric testing methods. But these tests lay different restrictions on the data.
If a statistical variable is of the nominal or ordinal type only, then it does not qualify to be parametrically tested. ...
Non-parametric test, parametric alternative and non-parametric counterparts are examined.