Contents
How do you know if a distribution is not normally distributed?
The P-Value is used to decide whether the difference is large enough to reject the null hypothesis:
- If the P-Value of the KS Test is larger than 0.05, we assume a normal distribution.
- If the P-Value of the KS Test is smaller than 0.05, we do not assume a normal distribution.
How do you proceed with non normal data?
Too many extreme values in a data set will result in a skewed distribution. Normality of data can be achieved by cleaning the data. This involves determining measurement errors, data-entry errors and outliers, and removing them from the data for valid reasons.
When does the distribution of data become an issue?
If a practitioner is not using such a specific tool, however, it is not important whether data is distributed normally. The distribution becomes an issue only when practitioners reach a point in a project where they want to use a statistical tool that requires normally distributed data and they do not have it.
What’s the problem if your data is not normal?
In probability theory, the normal (or Gaussian or Gauss or Laplace-Gauss) distribution is a very common continuous… So, what’s the problem? This is all hunky-dory, what is the issue? The issue is that often you may find a distribution for your specific data set, which may not satisfy Normality i.e. the properties of a Normal distribution.
What to do with data that is not normally distributed?
Cases that are not solvable by rearranging the data. The data set is only a part of all the data and all the data outside the tolerance borders is filtered. On from left to right: the original data, without the data above the tolerance border, data without min max tolerance and only data above the upper tolerance.
When to use the Gaussian distribution when data is not normal?
This can also be used in lieu of the Gaussian distribution when the data does not look Normal, but only when we have a high degree of confidence that the underlying process is composed of sub-processes which are completely independent of each other.