Test for normality

# If the road is not yet clear to you,we will show you the way how to get through.

## Test for normality

### In many statistical tests the assumption of normal distribution is made for variables measured at an interval or ratio scale. If more than 30 cases are part of the analysis, then, based on the central limit theorem, the variable might be assumed to be normally distributed.

A better way to check the assumptions of normality is to calculate the skewness and the kurtosis. Both values should lie between the limits of -0.05 and +0.05 or, when you are less strict, between -1.00 and +1.00.

Furthermore, two tests on normality can be applied: the Kolmogorov-Smirnov test and the Shapiro-Wilks test. Both test should be not-sigificant to conclude that the variable is normally distributed.

The most common reason for a variable not to be normally distributed is because there are some outliers. It is hard to give reasons why an outlier should be excluded. Do we have to change the data from the real world, because than it fits with the assumptions of statistics?

Analyze your data quick and easy with our SPSS tutorials.

## Mission

My goal is to teach you how to conduct good research.

Because:

Good research provides you with better information.
With better information, you can make better decisions.
With better decisions, you can create a healthier, wealthier and freer world,
for people, fauna and flora, for current and future generations.

That is why I think it is important that  you know how to do your research well.