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Parametric Hypothesis Tests

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.

 Hypothesis testing is covered in the MiC Quality Advanced Statistics Course. Try out our courses by taking the first module of the Primer in Statistics free of charge.
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