Parametric and Nonparametric Tests: A Brief Review

Banda Gerald; Tailoka Frank Patson

doi:doi:10.11648/j.ijsd.20210703.12

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Parametric and Nonparametric Tests: A Brief Review

Banda Gerald, Tailoka Frank Patson

Published in International Journal of Statistical Distributions and Applications (Volume 7, Issue 3)

Received: 2 April 2021 Accepted: 15 April 2021 Published: 28 October 2021

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Abstract

Context: Statistics is the cornerstone of markets, businesses, policy makers and other sectors that require analysis and interpretation of data. From generation-to-generation, statistics has proved useful in everyday life, not only that it helps improving the quality of life through counting and record keeping, but it also allows people to predict the future events and to make their own analysis. Before making a conclusion, data should be collected, analysed and interpreted. Evidence Acquisition: In this study, the paper reviewed parametric and nonparametric tests. Researchers sampled some articles where parametric and nonparametric tests were used without considering assumptions. Results: In this study, researchers provided a review of parametric tests; namely, independent sample t-test and dependent sample t-test, and nonparametric tests; namely, Mann-Whitney U test and Wilcoxon signed-rank test. The formulae for calculating parametric and nonparametric tests have been provided in the study. Procedures on how to conduct Mann-Whitney U test and Wilcoxon signed-rank test in SPSS have been written in this article. Test of normality has been discussed in brief as a key component in analysing parametric and nonparametric tests. Conclusions: Most of the studies that have been carried out have not been considering assumptions when analysing data using either parametric tests or nonparametric tests. This study looked at parametric and nonparametric tests. In parametric tests, the paper looked at independent and dependent sample t-test, while in nonparametric test, the paper looked at Mann-Whitney U test and Wilcoxon signed-rank test.

Published in	International Journal of Statistical Distributions and Applications (Volume 7, Issue 3)
DOI	10.11648/j.ijsd.20210703.12
Page(s)	78-82
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

Parametric, Nonparametric, Test of Normality, Statistics

References

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[6]	Umar, I. (2017). Assessing the Effect of Cooperative Learning on Financial Accounting Achievement among Secondary School Students. International Journal of Instruction. 10 (3) 31-46
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[9]	Asghar, G., & Saleh, Z. (2012). Normality Tests for Statistical Analysis: A Guide for Non-Statisticians. International Journal of Endocrinology Metabolism. 10 (2): 486-489. Doi: 10.5812/ijem.3505.
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[12]	Anna. H (2001). Mann-Whitney Test in not Just a Test of Medians: Differences in Spread can be Important. 323 (7309): 391–393. Doi: 10.1136/bmj.323.7309.391. [PMID: 1150943].
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[14]	Younis, S (2015). The Bread and Butter of Statistical Analysis ‘‘t-test’’. Uses and Misuses. Pakistan Journal of Medical Sciences. 31 (6): 1558-1559. Doi: 10.12669/pjms.316.8984. [PMCID: PMC4744321]. [PMID: 26870136].
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APA Style

Banda Gerald, Tailoka Frank Patson. (2021). Parametric and Nonparametric Tests: A Brief Review. International Journal of Statistical Distributions and Applications, 7(3), 78-82. https://doi.org/10.11648/j.ijsd.20210703.12

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ACS Style

Banda Gerald; Tailoka Frank Patson. Parametric and Nonparametric Tests: A Brief Review. Int. J. Stat. Distrib. Appl. 2021, 7(3), 78-82. doi: 10.11648/j.ijsd.20210703.12

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AMA Style

Banda Gerald, Tailoka Frank Patson. Parametric and Nonparametric Tests: A Brief Review. Int J Stat Distrib Appl. 2021;7(3):78-82. doi: 10.11648/j.ijsd.20210703.12

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@article{10.11648/j.ijsd.20210703.12,
  author = {Banda Gerald and Tailoka Frank Patson},
  title = {Parametric and Nonparametric Tests: A Brief Review},
  journal = {International Journal of Statistical Distributions and Applications},
  volume = {7},
  number = {3},
  pages = {78-82},
  doi = {10.11648/j.ijsd.20210703.12},
  url = {https://doi.org/10.11648/j.ijsd.20210703.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijsd.20210703.12},
  abstract = {Context: Statistics is the cornerstone of markets, businesses, policy makers and other sectors that require analysis and interpretation of data. From generation-to-generation, statistics has proved useful in everyday life, not only that it helps improving the quality of life through counting and record keeping, but it also allows people to predict the future events and to make their own analysis. Before making a conclusion, data should be collected, analysed and interpreted. Evidence Acquisition: In this study, the paper reviewed parametric and nonparametric tests. Researchers sampled some articles where parametric and nonparametric tests were used without considering assumptions. Results: In this study, researchers provided a review of parametric tests; namely, independent sample t-test and dependent sample t-test, and nonparametric tests; namely, Mann-Whitney U test and Wilcoxon signed-rank test. The formulae for calculating parametric and nonparametric tests have been provided in the study. Procedures on how to conduct Mann-Whitney U test and Wilcoxon signed-rank test in SPSS have been written in this article. Test of normality has been discussed in brief as a key component in analysing parametric and nonparametric tests. Conclusions: Most of the studies that have been carried out have not been considering assumptions when analysing data using either parametric tests or nonparametric tests. This study looked at parametric and nonparametric tests. In parametric tests, the paper looked at independent and dependent sample t-test, while in nonparametric test, the paper looked at Mann-Whitney U test and Wilcoxon signed-rank test.},
 year = {2021}
}

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TY - JOUR
T1 - Parametric and Nonparametric Tests: A Brief Review
AU - Banda Gerald
AU - Tailoka Frank Patson
Y1 - 2021/10/28
PY - 2021
N1 - https://doi.org/10.11648/j.ijsd.20210703.12
DO - 10.11648/j.ijsd.20210703.12
T2 - International Journal of Statistical Distributions and Applications
JF - International Journal of Statistical Distributions and Applications
JO - International Journal of Statistical Distributions and Applications
SP - 78
EP - 82
PB - Science Publishing Group
SN - 2472-3509
UR - https://doi.org/10.11648/j.ijsd.20210703.12
AB - Context: Statistics is the cornerstone of markets, businesses, policy makers and other sectors that require analysis and interpretation of data. From generation-to-generation, statistics has proved useful in everyday life, not only that it helps improving the quality of life through counting and record keeping, but it also allows people to predict the future events and to make their own analysis. Before making a conclusion, data should be collected, analysed and interpreted. Evidence Acquisition: In this study, the paper reviewed parametric and nonparametric tests. Researchers sampled some articles where parametric and nonparametric tests were used without considering assumptions. Results: In this study, researchers provided a review of parametric tests; namely, independent sample t-test and dependent sample t-test, and nonparametric tests; namely, Mann-Whitney U test and Wilcoxon signed-rank test. The formulae for calculating parametric and nonparametric tests have been provided in the study. Procedures on how to conduct Mann-Whitney U test and Wilcoxon signed-rank test in SPSS have been written in this article. Test of normality has been discussed in brief as a key component in analysing parametric and nonparametric tests. Conclusions: Most of the studies that have been carried out have not been considering assumptions when analysing data using either parametric tests or nonparametric tests. This study looked at parametric and nonparametric tests. In parametric tests, the paper looked at independent and dependent sample t-test, while in nonparametric test, the paper looked at Mann-Whitney U test and Wilcoxon signed-rank test.
VL - 7
IS - 3
ER -

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Author Information

Banda Gerald

Itimpi Campus, Mukuba University, Kitwe, Zambia
Tailoka Frank Patson

Itimpi Campus, Mukuba University, Kitwe, Zambia

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APA Style

Banda Gerald, Tailoka Frank Patson. (2021). Parametric and Nonparametric Tests: A Brief Review. International Journal of Statistical Distributions and Applications, 7(3), 78-82. https://doi.org/10.11648/j.ijsd.20210703.12

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ACS Style

Banda Gerald; Tailoka Frank Patson. Parametric and Nonparametric Tests: A Brief Review. Int. J. Stat. Distrib. Appl. 2021, 7(3), 78-82. doi: 10.11648/j.ijsd.20210703.12

Copy | Download

AMA Style

Banda Gerald, Tailoka Frank Patson. Parametric and Nonparametric Tests: A Brief Review. Int J Stat Distrib Appl. 2021;7(3):78-82. doi: 10.11648/j.ijsd.20210703.12

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@article{10.11648/j.ijsd.20210703.12,
  author = {Banda Gerald and Tailoka Frank Patson},
  title = {Parametric and Nonparametric Tests: A Brief Review},
  journal = {International Journal of Statistical Distributions and Applications},
  volume = {7},
  number = {3},
  pages = {78-82},
  doi = {10.11648/j.ijsd.20210703.12},
  url = {https://doi.org/10.11648/j.ijsd.20210703.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijsd.20210703.12},
  abstract = {Context: Statistics is the cornerstone of markets, businesses, policy makers and other sectors that require analysis and interpretation of data. From generation-to-generation, statistics has proved useful in everyday life, not only that it helps improving the quality of life through counting and record keeping, but it also allows people to predict the future events and to make their own analysis. Before making a conclusion, data should be collected, analysed and interpreted. Evidence Acquisition: In this study, the paper reviewed parametric and nonparametric tests. Researchers sampled some articles where parametric and nonparametric tests were used without considering assumptions. Results: In this study, researchers provided a review of parametric tests; namely, independent sample t-test and dependent sample t-test, and nonparametric tests; namely, Mann-Whitney U test and Wilcoxon signed-rank test. The formulae for calculating parametric and nonparametric tests have been provided in the study. Procedures on how to conduct Mann-Whitney U test and Wilcoxon signed-rank test in SPSS have been written in this article. Test of normality has been discussed in brief as a key component in analysing parametric and nonparametric tests. Conclusions: Most of the studies that have been carried out have not been considering assumptions when analysing data using either parametric tests or nonparametric tests. This study looked at parametric and nonparametric tests. In parametric tests, the paper looked at independent and dependent sample t-test, while in nonparametric test, the paper looked at Mann-Whitney U test and Wilcoxon signed-rank test.},
 year = {2021}
}

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