Difference between Parametric and Non-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 –
- 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:
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- 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.
In Non-Parametric methods, there 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 –
- Spearman correlation test
- Sign test for population means
- U-test for two independent means
Difference between Parametric and Non-Parametric Methods are as follows –
|S.No.||Parametric Methods||Non-Parametric Methods|
|1.||Parametric Methods uses a fixed number of parameters to build the model.||Non-Parametric Methods use the flexible number of parameters to build the model.|
|2.||Parametric analysis is to test group means.||A non-parametric analysis is to test medians.|
|3.||It is applicable only for variables.||It is applicable for both – Variable and Attribute.|
|4.||It always considers strong assumptions about data.||It generally fewer assumptions about data.|
|5.||Parametric Methods require lesser data than Non-Parametric Methods.||Non-Parametric Methods requires much more data than Parametric Methods.|
|6.||Parametric methods assumed to be a normal distribution.||There is no assumed distribution in non-parametric methods.|
|7.||Parametric data handles – Intervals data or ratio data.||But non-parametric methods handle original data.|
|8.||Here when we use parametric methods then the result or outputs generated can be easily affected by outliers.||When we use non-parametric methods then the result or outputs generated cannot be seriously affected by outliers.|
|9.||Parametric Methods can perform well in many situations but its performance is at peak (top) when the spread of each group is different.||Similarly, Non-Parametric Methods can perform well in many situations but its performance is at peak (top) when the spread of each group is the same.|
|10.||Parametric methods have more statistical power than Non-Parametric methods.||Non-parametric methods have less statistical power than Parametric methods.|
|11.||As far as the computation is considered these methods are computationally faster than the Non-Parametric methods.||As far as the computation is considered these methods are computationally faster than the Parametric methods.|
Logistic Regression, Naïve Bayes Model, etc.
KNN, Decision Tree Model, etc.