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 |
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