A Gentle Introduction to Nonparametric Statistics
Last Updated on November 10, 2019
A large portion of the field of statistics and statistical methods is dedicated to data where the distribution is known.
Samples of data where we already know or can easily identify the distribution of are called parametric data. Often, parametric is used to refer to data that was drawn from a Gaussian distribution in common usage. Data in which the distribution is unknown or cannot be easily identified is called nonparametric.
In the case where you are working with nonparametric data, specialized nonparametric statistical methods can be used that discard all information about the distribution. As such, these methods are often referred to as distribution-free methods.
In this tutorial, you will discover nonparametric statistics and their role in applied machine learning.
After completing this tutorial, you will know:
- The difference between parametric and nonparametric data.
- How to rank data in order to discard all information about the data’s distribution.
- Example of statistical methods that can be used for ranked data.
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