|
A parametric hypothesis test make assumptions about the underlying distribution of the population from which the sample is being drawn, and which is being investigated. This is typically that the population conforms to a normal distribution.
Parametric hypothesis tests include:
| ANOVA |
comparing the means of several (more than two) samples |
| Chi-Square Test |
testing 'goodness of fit' to an assumed distribution |
| Contingency Tables |
a variation on the chi-square test |
| F-test |
comparing variances |
| Proportion Test |
for differences between large or small proportions |
| t-test |
comparing the mean to a value, or the means of two samples |
| z-test |
as t-test but for large samples |
If the underlying distribution of the population is not known then a nonparametric test would be used. This would not be as powerful because it cannot use the predictable properties of the distribution.
|
