# Difference between Parametric and Non-Parametric Methods

• Difficulty Level : Easy
• Last Updated : 30 May, 2022

Parametric Methods: The basic idea behind the parametric method is that there is a set of fixed parameters that uses to determine a probability model that is used in Machine Learning as well. Parametric methods are those methods for which we priory knows that the population is normal, or if not then we can easily approximate it using a normal distribution which is possible by invoking the Central Limit Theorem. Parameters for using the normal distribution is as follows:

• Mean
• Standard Deviation

Eventually, the classification of a method to be parametric is completely depends on the presumptions that are made about a population. There are many parametric methods available some of them are:

• Confidence interval used for – population mean along with known standard deviation.
• The confidence interval is used for – population means along with the unknown standard deviation.
• The confidence interval for population variance.
• The confidence interval for the difference of two means, with unknown standard deviation.

Nonparametric Methods: The basic idea behind the parametric method is no need to make any assumption of parameters for the given population or the population we are studying. In fact, the methods don’t depend on the population. Here there is no fixed set of parameters are available, and also there is no distribution (normal distribution, etc.) of any kind is available for use. This is also the reason that nonparametric methods are also referred to as distribution-free methods. Nowadays Non-parametric methods are gaining popularity and an impact of influence some reasons behind this fame is:

• The main reason is that there is no need to be mannered while using parametric methods.
• The second important reason is that we do not need to make more and more assumptions about the population given (or taken) on which we are working on.
• Most of the nonparametric methods available are very easy to apply and to understand also i.e. the complexity is very low.

There are many nonparametric methods are available today but some of them are as follows:

• Spearman correlation test
• Sign test for population means
• U-test for two independent means

Difference between Parametric and Non-Parametric Methods are as follows:

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