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Difference Between Parametric and Nonparametric Test

Last updated on September 1, 2017 by Surbhi S

parametric-vs-nonparametric-testTo make the generalisation about the population from the sample, statistical tests are used. A statistical test is a formal technique that relies on the probability distribution, for reaching the conclusion concerning the reasonableness of the hypothesis. These hypothetical testing related to differences are classified as parametric and nonparametric tests.The parametric test is one which has information about the population parameter.

On the other hand, the nonparametric testΒ is one where the researcher has no idea regarding the population parameter. So, take a full read of this article, to know the significant differences between parametric and nonparametric test.

Content: Parametric Test Vs Nonparametric Test

  1. Comparison Chart
  2. Definition
  3. Key Differences
  4. Hypothesis Tests Hierarchy
  5. Equivalent Tests
  6. Conclusion

Comparison Chart

Basis for ComparisonParametric TestNonparametric Test
MeaningA statistical test, in which specific assumptions are made about the population parameter is known as parametric test. A statistical test used in the case of non-metric independent variables, is called non-parametric test.
Basis of test statisticDistributionArbitrary
Measurement levelInterval or ratioNominal or ordinal
Measure of central tendencyMeanMedian
Information about populationCompletely knownUnavailable
ApplicabilityVariablesVariables and Attributes
Correlation testPearsonSpearman

Definition of Parametric Test

The parametric test is the hypothesis test which provides generalisations for making statements about the mean of the parent population. A t-test based on Student’s t-statistic, which is often used in this regard.

The t-statistic rests on the underlying assumption that there is the normal distribution of variable and the mean in known or assumed to be known. The population variance is calculated for the sample. It is assumed that the variables of interest, in the population are measured on an interval scale.

Definition of Nonparametric Test

The nonparametric test is defined as the hypothesis test which is not based on underlying assumptions, i.e. it does not require population’s distribution to be denoted by specific parameters.

The test is mainly based on differences in medians. Hence, it is alternately known as the distribution-free test. The test assumes that the variables are measured on a nominal or ordinal level. It is used when the independent variables are non-metric.

Key Differences Between Parametric and Nonparametric Tests

The fundamental differences between parametric and nonparametric test are discussed in the following points:

  1. A statistical test, in which specific assumptions are made about the population parameter is known as the parametric test. A statistical test used in the case of non-metric independent variables is called nonparametric test.
  2. In the parametric test, the test statistic is based on distribution. On the other hand, the test statistic is arbitrary in the case of the nonparametric test.
  3. In the parametric test, it is assumed that the measurement of variables of interest is done on interval or ratio level. As opposed to the nonparametric test, wherein the variable of interest are measured on nominal or ordinal scale.
  4. In general, the measure of central tendency in the parametric test is mean, while in the case of the nonparametric test is median.
  5. In the parametric test, there is complete information about the population. Conversely, in the nonparametric test, there is no information about the population.
  6. The applicability of parametric test is for variables only, whereas nonparametric test applies to both variables and attributes.
  7. For measuring the degree of association between two quantitative variables, Pearson’s coefficient of correlation is used in the parametric test, while spearman’s rank correlation is used in the nonparametric test.

Hypothesis Tests Hierarchy

parametric vs nonparametric test

Equivalent Tests

Parametric TestNon-Parametric Test
Independent Sample t TestMann-Whitney test
Paired samples t testWilcoxon signed Rank test
One way Analysis of Variance (ANOVA)Kruskal Wallis Test
One way repeated measures Analysis of VarianceFriedman's ANOVA

Conclusion

To make a choice between parametric and the nonparametric test is not easy for a researcher conducting statistical analysis. For performing hypothesis, if the information about the population is completely known, by way of parameters, then the test is said to be parametric test whereas, if there is no knowledge about population and it is needed to test the hypothesis on population, then the test conducted is considered as the nonparametric test.

Related Differences

  • Difference Between Theme and Topic
  • Difference Between Motivation and Morale
  • Difference Between Audit and Review
  • Difference Between Factors and Multiples
  • Difference Between Cost Sheet and Production Account

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Comments

  1. Poorvi says

    December 1, 2016 at 3:49 pm

    Very nice article. students can clearly understand the actual concept.

    Reply
  2. Osoba Adunola says

    December 5, 2016 at 3:11 pm

    The information is very detailed and easy to grab. Thanks

    Reply
  3. Sue Smith says

    December 15, 2016 at 11:37 am

    This is excellent. The flowchart was really helpful. Thank you.

    Reply
    • Surbhi S says

      December 15, 2016 at 11:52 am

      We are really contented with your views, this means a lot, keep sharing.

      Reply
  4. Nyadenga wellington says

    April 10, 2017 at 6:30 pm

    Great!. This clears off subject anxiety. keep sharing

    Reply
  5. tariro says

    May 29, 2017 at 2:23 am

    Thank u guys for simplifying this for us…. then u wonder why lecturers make it sound so hectic when things can be explained in such an easy to grasp way saving us the anxiety n pressure

    Reply
  6. Madan says

    July 11, 2017 at 8:13 am

    thank you

    Reply
  7. MD. MEHEDI HASAN says

    July 25, 2017 at 6:08 pm

    These informations are very helpful to understand the concepts.
    Thanks

    Reply
  8. Suparna says

    October 20, 2017 at 10:40 am

    Thank you very much for the information and the explanation you’ve given… It helped me to understand the topic much better.

    Reply
  9. Morrel says

    November 28, 2017 at 1:38 pm

    Thank you!

    Reply
  10. Super Ball says

    November 29, 2017 at 9:05 pm

    Information is clear to understand, very helpful. Thank you πŸ™‚

    Reply
  11. Kimnna says

    December 13, 2017 at 12:05 am

    This is super helpful!
    It is well detailed and easy to understand.

    Reply
  12. kakai brian says

    February 6, 2018 at 1:31 pm

    am liking this site
    it is great

    Reply
  13. Lillian Ramos says

    February 11, 2018 at 9:12 am

    This was extremely helpful on a very technical and difficult subject such as statistics.

    Reply
  14. Keith says

    March 13, 2018 at 2:08 pm

    Please help me ….. I fail to understand what is meant if the question reads as follows:

    State the parametric and non-parametric equivalent of the Wilcoxon Signed Rank Test

    Reply
  15. ABAH Augustine says

    March 24, 2018 at 8:05 pm

    very informative and educating. nice one

    Reply
    • Surbhi S says

      May 11, 2018 at 3:36 pm

      Millions of thanks to all the readers of the page, for liking and sharing your valuable opinions with us, keep reading. πŸ™‚

      Reply
  16. Sean says

    June 30, 2018 at 5:18 pm

    This article is really helpful…
    Cheers to Surbhi S for creating this article and pls do continue on creating articles like this…

    Reply
  17. Prakash Mistri says

    July 23, 2018 at 8:25 pm

    very much effective documents. This material provides very good clarity on the parametric and non-parametric difference.

    Reply
  18. NORA says

    August 24, 2018 at 9:24 pm

    thank you so much for making this more simplified

    Reply
  19. hanadi says

    September 13, 2018 at 1:47 am

    thank you for the simple yet detailed elaboration. million thanks you saved me.

    Reply
  20. Valsamma Cherian says

    September 18, 2018 at 1:12 pm

    Thank you . For this is very simple and apt information

    Reply
  21. kinza batool malik says

    January 8, 2019 at 6:42 pm

    its quite helpful and easy to understand..

    Reply
  22. Sally Morton says

    February 28, 2019 at 10:17 pm

    Thank you so much for this article, especially the Hypothesis Test Hierarchy chart. I am reviewing statistics, and this chart serves as a roadmap.

    Reply
  23. Shahzad rauf says

    April 1, 2019 at 1:27 pm

    Very helpful, I wll say awsome

    Reply
  24. Vishnu says

    May 24, 2019 at 11:29 pm

    Very good content and clear explanation

    Reply
  25. Aqib says

    June 17, 2019 at 3:42 am

    Thanks for a wonderfully easy explanation….

    Reply
  26. DPN says

    June 20, 2019 at 8:00 am

    awesome

    Reply
  27. Omony Kamau says

    October 7, 2019 at 7:08 pm

    Very precise and to the points. I just like it

    Reply
  28. Ashenafi Tadesse says

    June 15, 2020 at 7:45 pm

    Yes!

    Reply
  29. savita says

    December 13, 2020 at 10:39 am

    thanku very much….. very very helpful especially the hypothesis test chart

    Reply
  30. Stella Nwodo says

    December 17, 2020 at 8:43 am

    This document is very simplified, thank you for the knowledge.

    Reply
  31. nompe jeminah sebeko cekwana says

    May 23, 2021 at 4:48 pm

    Thank you very much, this information is clear and effective.

    Reply
  32. S.Sadiq Al-Alawi says

    January 23, 2023 at 1:02 am

    Thank you, very helpful, simply written, straight forward, easy to understand. I really liked it.

    Reply

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