### Abstract

Different types of numerical data can be collected in a scientific investigation and the choice of statistical analysis will often depend on the distribution of the data. A basic distinction between variables is whether they are ‘parametric’ or ‘non-parametric’. When a variable is parametric, the data come from a symmetrically shaped distribution known as the ‘Gaussian’ or ‘normal distribution’ whereas non-parametric variables may have a distribution which deviates markedly in shape from normal. This article describes several aspects of the problem of non-normality including: (1) how to test for two common types of deviation from a normal distribution, viz., ‘skew’ and ‘kurtosis’, (2) how to fit the normal distribution to a sample of data, (3) the transformation of non-normally distributed data and scores, and (4) commonly used ‘non-parametric’ statistics which can be used in a variety of circumstances.

Original language | English |
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Pages (from-to) | 26 |

Number of pages | 1 |

Journal | Optometry Today |

Volume | 51 |

Issue number | 16 |

Publication status | Published - 19 Aug 2011 |

### Keywords

- numerical data
- scientific investigation
- choice
- statistical analysis
- distribution of data
- parametric
- distribution

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## Cite this

Armstrong, R. A. (2011). Non-normally distributed data and non-parametric statistics.

*Optometry Today*,*51*(16), 26.