### Abstract

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

Pages | 46-47 |

Number of pages | 2 |

Volume | 11 |

Specialist publication | Microbiologist |

Publication status | Published - Dec 2010 |

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### Keywords

- analysis of variance
- data
- non-parametric analysis of variance
- Kruskal-Wallis test
- Friedmann’s analysis of variance

### Cite this

*Microbiologist*,

*11*, 46-47.

}

*Microbiologist*, vol. 11, pp. 46-47.

**Statnote 23: Non-parametric analysis of variance (ANOVA).** / Hilton, Anthony; Armstrong, Richard A.

Research output: Contribution to specialist publication › Article

TY - GEN

T1 - Statnote 23: Non-parametric analysis of variance (ANOVA)

AU - Hilton, Anthony

AU - Armstrong, Richard A.

PY - 2010/12

Y1 - 2010/12

N2 - To carry out an analysis of variance, several assumptions are made about the nature of the experimental data which have to be at least approximately true for the tests to be valid. One of the most important of these assumptions is that a measured quantity must be a parametric variable, i.e., a member of a normally distributed population. If the data are not normally distributed, then one method of approach is to transform the data to a different scale so that the new variable is more likely to be normally distributed. An alternative method, however, is to use a non-parametric analysis of variance. There are a limited number of such tests available but two useful tests are described in this Statnote, viz., the Kruskal-Wallis test and Friedmann’s analysis of variance.

AB - To carry out an analysis of variance, several assumptions are made about the nature of the experimental data which have to be at least approximately true for the tests to be valid. One of the most important of these assumptions is that a measured quantity must be a parametric variable, i.e., a member of a normally distributed population. If the data are not normally distributed, then one method of approach is to transform the data to a different scale so that the new variable is more likely to be normally distributed. An alternative method, however, is to use a non-parametric analysis of variance. There are a limited number of such tests available but two useful tests are described in this Statnote, viz., the Kruskal-Wallis test and Friedmann’s analysis of variance.

KW - analysis of variance

KW - data

KW - non-parametric analysis of variance

KW - Kruskal-Wallis test

KW - Friedmann’s analysis of variance

UR - http://issuu.com/societyforappliedmicrobiology/docs/dec2010_micro_final

M3 - Article

VL - 11

SP - 46

EP - 47

JO - Microbiologist

JF - Microbiologist

SN - 1479-2699

ER -