The next figure shows a normal distribution with sample a size of 208. The same data is shown in a histogram, probability plot, dot plot and a box blot. The figure below shows a normal distribution with a sample size of 27. We can assess the results by looking at the resulting p value. Test statistic: A 2 = - N – S, where and F is the cumulative distribution function of the specified distribution. H a: The data don’t follow a normal distribution. H 0: The data follow a normal distribution. Anderson-Darling test’s null hypothesis is “The distribution is normal.” Minitab uses the Anderson-Darling test, which compares the actual distribution to a theoretical normal distribution. Fortunately, we can also use Minitab Statistical Software to assess the normality of data. That's fine for a small data set, but nobody wants to plot hundreds or thousands of data points by hand. ![]() We then plot the results as shown in the figure below. Using the numbers 16, 21, 20, 19, 18 and 15, we would construct a normal probability plot by first creating the table shown below. ![]() Those of us who are a bit old-fashioned can construct a probability plot by hand, by plotting the order values (j) against the observed cumulative frequency (j- 0.5/n). What can we do if the assumption of normality is critical to so many statistical methods? We can construct a probability plot to test this assumption. It would be very risky to monitor a process with SPC charts created with data that violated the assumption of normality. Statistical Process Control (SPC) requires either normally distributed data or a transformation must be performed on the data. This could result in a capital investment for equipment that actually results in higher costs in the long run. For example, a Z test may indicate a new process is more efficient than an older process when this is not true. Violating the assumption of normality can result in incorrect conclusions. Simplilearn offers Minitab training course online with Statistics.Many statistical tests assume the data being tested came from a normal distribution. Read more: Process Capability Analysis: Minitab with Statistics To know more about Normality Test, you can explore Simplilearn’s Minitab with Statistics Training. 05, we can assume the “Before” data is normal. Here we can notice that since the P value is greater than. Once we click ok, Minitab generates the probability plot in a separate window. While there are multiple kinds of normality tests available, the Anderson Darling Test is the most reliable and commonly used test. Now we click on Anderson-Darling and then click on OK. Double click on before in the left hand side box to select it. In this example, let us test the Column which has before, data for normality. Click on Normality Test then enter the variables on the respective columns. Go to Start menu and then move to Basic Statistics. Go to File Menu, click Open Project and then load the file including Cholesterol levels at fasting. Example of conducting a Normality Test Taking the example of Cholesterol levels at fasting, before breakfast and after breakfast levels, let’s conduct a normality test. After clicking OK, Minitab generates the probability plot in a separate window. Step 3: Click on Normality Test and then enter the variables on the respective columns. Step 2: Go to Start menu and then move to Basic Statistics. ![]() Step 1: Go to File menu, click Open Project and then load the data to be analyzed. Let’s have a look at the steps to perform a normality test using Minitab. Minitab has statistical tools that allow one to perform statistical calculations with ease. One can conduct a Normality test using Minitab. Many statistical analyses require that the data come from normally distributed populations. The normal distribution is the most common statistical distribution because approximate normality arises naturally in many physical, biological, and social measurement situations. A normal distribution is a bell-shaped curve that is symmetric about its mean. Normality Test helps one to determine whether a data is following a normal distribution or not. Normality is one of the major concepts in statistics used for various statistical calculations.
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