### Abstract

Original language | English |
---|---|

Pages (from-to) | 26 |

Number of pages | 1 |

Journal | Optometry Today |

Volume | 51 |

Issue number | 16 |

Publication status | Published - 19 Aug 2011 |

### Fingerprint

### Keywords

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

### Cite this

*Optometry Today*,

*51*(16), 26.

}

*Optometry Today*, vol. 51, no. 16, pp. 26.

**Non-normally distributed data and non-parametric statistics.** / Armstrong, Richard A.

Research output: Contribution to journal › Article

TY - JOUR

T1 - Non-normally distributed data and non-parametric statistics

AU - Armstrong, Richard A.

PY - 2011/8/19

Y1 - 2011/8/19

N2 - 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.

AB - 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.

KW - numerical data

KW - scientific investigation

KW - choice

KW - statistical analysis

KW - distribution of data

KW - parametric

KW - distribution

M3 - Article

VL - 51

SP - 26

JO - Optometry Today

JF - Optometry Today

SN - 0268-5485

IS - 16

ER -