What to do with not normally distributed Data

Step 1

Do normally check Anderson Darling normality test with a high p value you can assume normality of the data. Develve assumes a p value above 0.10 as normally distributed.

Step 2

Find out why the data is possible not normally distributed.

Mixture of various distributions

• Samples from different batches
• Samples from different dates
• Samples form different mold cavities

Try to sort the data. This is possible in the DOE mode in Develve.

Example

In this example the data is sorted on the two production lines 1 and 2 and after sorting the data of the both production lines are normally distributed Column B and C, and the original data is in column A.

Data file

Drift in measurement system

Look to the Time graph.

Data file

Cases that are not solvable by rearranging the data.

Sorted data

The data set is only a part of all the data and all the data outside the tolerance borders is filtered.

On the left the original data, in the middle data without data above the tolerance border and to the right data without data outside the min and max tolerance.

Data is close to zero or a other limit

Data close to the zero or the optimum will tend to skew to the left.

Data is following a other distribution

Step 3

If the case is not solvable by rearranging the data there are two options. Transform data or use a test that is not based on a normally assumption, or use a test not based on normal assumption.

Transform

Box-Cox transformation

Test not based on normal assumption

• 1 sample Wilcoxon median test
• 2 sample Mann-Whitney median test
• Variation Levene test