Non Parametric test: Overview, Questions, Preparation

Non-parametric tests are used to identify patterns of diverse populations. It is constructed without relation to some specific parametric distribution of probability. Distribution-free experiments are often considered non-parametric because they do not include any population distribution. Non-parametric tests are all mathematical techniques that do not create assumptions about the distribution of factors or parameters to be tested. Where there are biased data, the non-parametric experiment depends on techniques that do not rely on any specific distribution. 

The term non-parametric does not imply these models do not have any parameters. In truth, the features and various varieties of the criteria aren't constrained and not fixed in stone. Therefore, these structures are considered general models.

Non-parametric T-test 

When uncertain hypotheses occur inside a sample, we use Non-parametric tests which are called parametric counterparts. When results do not obey a regular distribution or are calculated on an ordinal basis, nonparametric statistical tests may be used for interpretation. A parametric test is used on naturally distributed data, and a non-parametric test is used on warped data. 

Non-parametric Paired T-test 

The two samples would be compared using the paired t-test and would come from the same party. The t-test is acceptable because the independent variables have two degrees, and they are evaluated with repeated steps. 

Important non-parametric data include: 

Kruskal-Wallis Test

Friedman Test

1-Sample Sign Test

Mood’s Median Test

Spearman Rank Correlation

Mann-Kendall Trend Test

Mann-Whitney Test

Advantages and Drawbacks of the Non-parametric Test 

The benefits of the non-parametric evaluation are: 

• Easily understood. 
• Simple calculations. 
• There is no assumption of distribution necessary. 
• For any form of data. 

The disadvantages of the non-parametric test are:

• Less effective than a parametric test. 
• Results are not able to be reliably measured because of the absence of corrections.

Applications of Non-parametric Statistical Methods 

When non-parametric tests are used, the following criteria apply: 

• When predicted parameters are not reached. 
• The null hypothesis may be checked without any distribution of it. 
• To do fast analysis. 
• When quantitative evidence is available.
• Weightage of Non-parametric Test in Class 12

This concept is taught under chapter probability. You will learn about the parametric function and its types. The weightage of this chapter is 6 marks in the final exam.

1.List an example of a nonparametric testing procedure. 

Solution. The only nonparametric exam a subject is going to have to perform is a Chi-Square test. There are also others such as: For example, non-parametric alternatives to One way ANOVA and Two-sample t-test are the Kruskal-Willis test and Mann-Whitney U test. 

2. Is the chi-square test nonparametric? 
Solution. Chi-square is non-parametric since it does not presume a fixed distribution for the data. Non-parametric tests must be utilised where one or all of the following criteria apply: all factors are trivial or ordinal.

Q: What non-parametric test is equivalent to a t-test?

Q: What does a non-parametric test mean?

Q: What distinguishes parametric tests from nonparametric tests?

Q: Are non-parametric statistics influenced by assumptions?

Q: Why use a non-parametric statistical test?