How to Perform a Normality Test on Minitab

Thứ bảy - 27/04/2024 01:10

Before you start performing any statistical analysis on the given data, it is important to identify if the data follows normal distribution. If the given data follows normal distribution, you can make use of parametric tests (test of...

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Before you start performing any statistical analysis on the given data, it is important to identify if the data follows normal distribution. If the given data follows normal distribution, you can make use of parametric tests (test of means) for further levels of statistical analysis. If the given data does not follow normal distribution, you would then need to make use of non-parametric tests (test of medians). As we all know, parametric tests are more powerful than non-parametric tests. Hence, checking the normality of the given data becomes all the more important.

Steps

A good way to perform any statistical analysis is to begin by writing the hypothesis. For normality test, the null hypothesis is “Data follows a normal distribution” and alternate hypothesis is “Data does not follow a normal distribution”.

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Select and copy the data from spreadsheet on which you want to perform the normality test.

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Open Minitab and paste the data in Minitab worksheet.

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In the menu bar of Minitab, click on Stat.

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A small window named “Normality Test” will pop-up on the screen. Click on the available option inside the white box and then Click “Select”.

Be aware that the “Variable” tab will have the name of selected data.

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Also be aware that “Anderson-Darling” is already selected under “Tests for Normality”. Anderson-Darling is the most widely used Normality test. Hence, in Minitab, the default selection of Tests for Normality is “Anderson-Darling”.

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A normal probability plot will appear on the screen.
Please observe if the p-value displayed in the normal probability plot is greater than 0.05 or is it lesser than 0.05.

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As described in the step of writing the hypothesis, if we fail to reject the null hypothesis, the inference will be “Data follows a normal distribution”. If we reject the null hypothesis, the inference will be “Data does not follow a normal distribution”. Let’s link the p-value to the written hypothesis.

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If the p-value observed in normal probability plot is greater than 0.05, we fail to reject the null hypothesis. Thus the inference is “Data follows a normal distribution”.

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If the p-value observed in normal probability plot is less than 0.05, we reject the null hypothesis. Thus the inference is “Data does not follow a normal distribution”.