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. |