| 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. |
