Dealing with Non-Normal Data

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 ToolsArne 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

One Response to Dealing with Non-Normal Data

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