Parametric versus non-parametric

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:

Non-parametric statistics

Descriptive

NameFor whatNotes
ModeCentral tendancyGreatest frequency
MedianCentral tendancy50% split of distribution
RangeDistributionlowest and highest value
Association

NameFor whatNotes
Spearman's RhoCorrelationbased on rank order of data
Kendall's TauCorrelation
Chi squareTabled data

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