Normal distribution, also called Gaussian distribution, is probably the most important distribution related to continuous data from a statistical analysis standpoint. It is sometimes called the “bell curve,” although the tonal qualities of such a bell would be less than pleasing. A normal, or Gaussian, distribution is depicted below.
Normal data is shaped symmetrically surrounding the mean, represented above by the x-bar line. A normal curve is beneficial for determining the probability that a given data point in a population will fall inside a certain range within the distribution.
Since the normality test will examine the probability of data falling inside the normal distribution range, we can use the second method to check whether the data falls under normal distribution or not by using the normal probability plot as the visual inference of normality test in Excel.
This method is more straightforward than the Chi-Sq Goodness of Fit test. It is suitable for beginner analysts to understand and examine the data and whether it falls under the normal distribution.
A normal probability plot can determine if small data sets come from a normal distribution. This involves using the probability properties of the normal distribution. We will eventually make a plot that we hope is linear. We will demonstrate the procedure using the data below.
This tutorial will show how to create the normal probability plot step by step.
Step 1: Let’s create a dataset
We will use a fake data set and sort the data in ascending order.
Step 2: Calculate the z-values
Next, we’ll use the following formula to calculate the z-value that corresponds to the first data value