What are the key differences between parametric and
non-parametric tests? Provide one example of a parametric test and
one example of a non-parametric test.
An
advantage of non-parametric tests includes:
a. Good power compared to parametric tests
b. Set up to test hypotheses and estimate effect size
c. Very few assumptions for the distribution of the data
d. Allows for analysis of large continuous scale data
sets
Match these non-parametric statistical tests with their
parametric counterpart by putting the corresponding letter on the
line.
_____ Friedman test
_____ Kruskal-Wallis H test
_____ Mann-Whitney U test
_____ Wilcoxon Signed-Ranks t test
A: Paired-sample t-test
B: Independent-sample t-test
C: One-way ANOVA, independent samples
D: One-way ANOVA, repeated measures
When should you use non-parametric tests of statistical
significance? When is it inappropriate to use non-parametric
statistical tests? Describe what is meant by the phrase: "Power of
a a statistical test". Are non-parametric statistical procedures as
powerful as parametric statistical procedures?
6. Parametric tests usually have more statistical power than
non-parametric tests.
True or False
5. A post hoc test does not need to be performed when an ANOVA
produces a statistically significant F value.
True or False
4. In the case of a hypothesis t test, population mean is
known.
True or False
Are
one way ANOVA, Bartletts test and Levene test parametric or non
parametric hipothesis tests and why?
I need some explanation on this as i dont quite understand
what it means to be a parametric or non parametric test. Thank
you?