1.Normality Tests for Statistical Analysis
This easy tutorial will show you how to run the normality test in SPSS, and how to interpret the result. In another word, The aim of this commentary is to overview checking for normality in statistical analysis using SPSS.
A normal distribution is a common probability distribution. In addition, It has a shape often referred to as a “bell curve.”
Many everyday data sets typically follow a normal distribution: for example, the heights of adult humans, the scores on a test given to a large class, errors in measurements. That is to say, the normal distribution is always symmetrical about the mean. (Source)
Statistical errors are common in scientific literature and about 50% of the published articles have at least one error. In addition, The assumption of normality needs to be checked for many statistical procedures, namely parametric tests, for the reason that their validity depends on it. (Source)
The prerequisite for most of the statistical tests is normal data distribution. Therefore, We use tests of normality to assess whether data is normally distributed or not. If the data is normally distributed so we can use the parametric tests. In another case, it is better to use nonparametric tests. We can also use plots to assess data distribution.
We use the Shapiro-Wilk test when we have a small sample size (N < 50) and Kolmogorov-Smirnov test when we have a large sample size (N > 50).
2. An Example: Normality Test in SPSS
We collected data from 32 workers about their age and height in centimeters. To examine whether data for age and height are normally distributed, we used tests of normality.
Null hypothesis:
Data is normally distributed.
Alternative hypothesis:
Data is not normally distributed.
Finally, This easy tutorial will show you how to run the normality test in SPSS, and how to interpret the result.
