How often do you have to deal with non-normal data? Do you know what to do with it? In his article “**Dealing with Non-normal Data: Strategies and Tools**” Arne Buthmann explains the common reasons for non-normal data and how to handle it.

#### Addressing Reasons for Non-normality

**Reason 1: **Extreme Values

**Reason 2: **Overlap of Two or More Processes

**Reason 3: **Insufficient Data Discrimination

**Reason 4: **Sorted Data

**Reason 5: **Values Close to Zero or a Natural Limit

**Reason 6: **Data Follows a Different Distribution

#### No Normality Required

He states that “Some statistical tools do not require normally distributed data. To help practitioners understand when and how these tools can be used, the table below shows a comparison of tools that do not require normal distribution with their normal-distribution equivalents.”

Comparison of Statistical Analysis Tools for Normally and Non-Normally Distributed Data | ||

Tools for Normally Distributed Data |
Equivalent Tools for Non-Normally Distributed Data |
Distribution Required |

T-test | Mann-Whitney test; Mood’s median test; Kruskal-Wallis test | Any |

ANOVA | Mood’s median test; Kruskal-Wallis test | Any |

Paired t-test | One-sample sign test | Any |

F-test; Bartlett’s test | Levene’s test | Any |

Individuals control chart | Run Chart | Any |

Cp/Cpk analysis | Cp/Cpk analysis | Weibull; log-normal; largest extreme value; Poisson; exponential; binomial |