THE USE OF TWO-SAMPLE t-TEST IN THE REAL DATA
Keywords:
grade points average (GPA), descriptive statistics, pooled variance, two-sample t-test, significant difference.DOI:
https://doi.org/10.17654/0972361722071Abstract
The t-test is one of the most commonly used statistical methods. It was developed and accredited by William Gosset, Karl Pearson and R. Fisher in the 19th century. The test was further developed to the two-sample test (Snedecor and Cochran [10]) which is used to determine whether two populations are equal. A common application of the two-sample t-test is to test whether a process or treatment is superior to a current process or treatment. In this research, using the two-sample t-test, a comparison between the students in the three grades of the Department of Mathematics Education, Tishk International University, is made to see whether there is a significant difference between the grade point averages (GPAs) for the second, third and fourth grades, in addition to gender. The result showed that there is no significant difference on the average scores between grade 4 and grade 3, also no significant difference between grade 4 and grade 2, but there is a significant difference between grade 3 and grade 2. Similarly, there is no significant difference on the average scores due to gender.
Received: August 10, 2022
Accepted: September 15, 2022
References
C.-H. Chang and N. Pal, A revisit to the Behrens-Fisher problem: comparison of five test methods, Comm. Statist. Simulation Comput. 37(6) (2008), 1064-1085.
A. W. F. Edwards and R. A. Fisher, Statistical methods for research workers, Landmark Writings in Western Mathematics: Case Studies, I. Grattan-Guinness, ed., Elsevier, Amsterdam, 2005, pp. 1640-1940. doi: 10.1016/B978-044450871-3/50148-0.
S. Foster and K. Gerald, Review of the two sample t tests, Nurse Anesthesia 1(1) (1990), 38-40.
N. A. Heckert, J. J. Filliben, C. M. Croarkin, B. Hembree, W. F. Guthrie, P. Tobias and J. Prinz, Handbook 151: NIST/SEMATECH e-Handbook of Statistical Methods, 2002.
F. Bacchus, AIPS 2000 planning competition: The Fifth International Conference on Artificial Intelligence Planning and Scheduling Systems, AI Magazine 22(3) (2001), 47-56.
F. Bacchus and M. Ady, Planning with resources and concurrency: a forward chaining approach, IJCAI, Vol. 1, 2001, pp. 417-424.
A. Maüll Miquel, Functional stability of activated graphene-based electrodes after sterilization with ethylene oxide, Bachelor’s thesis UPF, 2021.
J. H. McDonald, Handbook of Biological Statistics, Vol. 2, Sparky House Publishing, Baltimore, MD, 2009.
R. Peck, C. Olsen and J. Devore, Introduction to Statistics and Data Analysis, Cengage Learning, Boston, 2012.
G. Snedecor and W. Cochran, Arc sine transformation for proportions, Statistical Methods, 8th ed., Iowa State University Press, Ames, 1989, pp. 289-290.
M. Xu, D. Fralick, J. Z. Zheng, B. Wang, X. M. Tu and C. Feng, The differences and similarities between two-sample t-test and paired t-test, Shanghai Archives of Psychiatry 29(3) (2017), 184-188.
K. H. Yim, F. S. Nahm, K. A. Han and S. Y. Park, Analysis of statistical methods and errors in the articles published in the Korean Journal of Pain, The Korean Journal of Pain 23(1) (2010), 35-41.
Downloads
Published
Issue
Section
License
Copyright (c) 2022 Pushpa Publishing House, Prayagraj, India

This work is licensed under a Creative Commons Attribution 4.0 International License.
____________________________
Attribution: Credit Pushpa Publishing House as the original publisher, including title and author(s) if applicable.
No Derivatives: Modifying or creating derivative works not allowed without written permission.
Contact Pushpa Publishing House for more info or permissions.
Journal Impact Factor: 