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An Introduction to the Anderson Darling Normality Test

In statistics, normality tests are used to assess whether a data set is well-modelled by a normal distribution. There are a variety of normality tests, but in this blog post, we’ll focus on the Anderson Darling test. Keep reading to learn more about this test and how to interpret the results.

What is the Anderson Darling Test?

 

The Anderson Darling test is a statistical test that can be used to assess whether a data set follows a normal distribution. The test is based on the idea that if a data set is normally distributed, then the maximum difference between the cumulative distribution function of the data and the normal distribution should be minimized.

The Anderson Darling test is one of the most powerful normality tests because it is less sensitive to outliers than other tests. However, this power comes at a cost; the Anderson Darling test also has a higher Type I error rate than other normality tests.

How to Interpret the Results of the Test

 

There are two main things you need to look at when interpreting the results of an Anderson Darling normality test:

The p-value: This is the probability that you would observe a test statistic as extreme as or more extreme than the one you actually observed, given that the null hypothesis is true. A small p-value (generally anything below 0.05) means that you can reject the null hypothesis and conclude that the data is not normally distributed.

The critical values: These are percentage points of the distribution under the null hypothesis. If your test statistic is greater than or equal to one of these values, you can reject the null hypothesis and conclude that the data is not normally distributed.

 

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Conclusion:

 

In conclusion, The Anderson Darling Normality Test is a statistical tool used to evaluate whether a Normal Distribution can reasonably model a dataset. The Test accepts or rejects this assumption by identifying outliers in the dataset that deviate from what would be expected under a Normal Distribution. This information can help researchers better understand their data and determine which type of Statistical Analysis would best suit their study moving forward.

The Anderson Darlington Normality Test is covered as part of our Lean Six Sigma Black Belt Course and a shorter introduction within our Green Belt Course.

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