A potential source of confusion in working out what statistics to use in analysing data is whether your data allows for parametric or non-parametric statistics.
The importance of this issue cannot be underestimated!
If you get it wrong you risk using an incorrect statistical procedure or you may use a less powerful procedure.
Non-paramteric statistical procedures are less powerful because they use less information in their calulation. For example, a parametric correlation uses information about the mean and deviation from the mean while a non-parametric correlation will use only the ordinal position of pairs of scores.
The basic distinction for paramteric versus non-parametric is:
There are other considerations which have to be taken into account:
|Mode||Central tendancy||Greatest frequency|
|Median||Central tendancy||50% split of distribution|
|Range||Distribution||lowest and highest value|
|Spearman's Rho||Correlation||based on rank order of data|
|Chi square||Tabled data|