# T-Test Application and Interpretation ﻿

T-Test Application and Interpretation

QUESTION
Complete this assessment using the Data Analysis and Application Template [DOC] (also known as the DAA Template).
• Refer to IBM SPSS Step-By-Step Guide: t-Tests [PDF] for additional information on using SPSS for this assessment.
• Review the Copy/Export Output Instructions [PDF] for help copying SPSS output into your DAA Template.
• Use the Data Set Instructions [PDF] for information on the data set.
• Refer to the Course Study Guide [PDF] for information on analyses and interpretation.
The grades.sav file is a sample SPSS data set. The data represent a teacher’s recording of student demographics and performance on quizzes and a final exam across three sections of the course. Each section consists of 35 students (N = 105). There are 21 variables in grades.sav.
You will analyze the following variables in the grades.sav data set:
T-Test Application and Interpretation

T-Test Application and Interpretation

Name
Capella University
Due date

Data Analysis Plan
Variables Definitions
SPSS Variable Definition Measure
Gender female =1; male =2 categorical
GPA Previous grade point average Continuous

Research question.
Does gender affect the GPA score of a student?
Hypothesis
H0: male students record higher GPA compared to female students.
H1: Mal students do not record higher GPA than students.
Testing Assumptions

Descriptive
Statistic Std. Error
GPA Mean 2.8622 .06955
Median 2.8400
Variance .508
Std. Deviation .71266
Minimum 1.08
Maximum 4.00
Range 2.92
Interquartile Range 1.19
Skewness -.220 .236
Kurtosis -.688 .467
The skewness and kurtosis of the GPA score is below +1 and therefore is within the range of normality and can be said to be normally distributed.

Results and Interpretation

Independent Samples Test
Levene’s Test for Equality of Variances t-test for Equality of Means
F Sig. t df Sig. (2-tailed) Mean Difference Std. Error Difference 95% Confidence Interval of the Difference
Lower Upper
gpa Equal variances assumed .095 .758 1.999 103 .048 .28090 .14055 .00215 .55965
Equal variances not assumed 1.961 79.985 .053 .28090 .14326 -.00419 .56599
The sigma(2-tailed) =0.048 which is less than 0.05, this confirms that there is a significant difference in the mean GPA score of the between the male and the female students.
Statistical Conclusions
Looking at the group statistics table below, male students have higher mean GPA as compared to the female students. We can therefore accept the null hypothesis at 95% confidence level and conclude that, the mean GPA of the male student is significantly greater than that of the female students.
Group Statistics
gender N Mean Std. Deviation Std. Error Mean
gpa Male 64 2.9719 .67822 .08478
Female 41 2.6910 .73942 .11548

Application
T-test analysis is basically a test Used to determine if there is a mean difference in the effect of a particular event on two groups, (Kim, T., 2015). In medicine, it is always used in drug test and comparison of the effect of a drug on different group of people or two deferent drugs.
During drug tests, there are always the control and the test group, in order to understand the if there is a difference in the mean effect, t-test is the most appropriate test for such a test. This makes the use of t-test very important in the field or medical research.

References
Kim, T. K. (2015). T test as a parametric statistic. Korean journal of anesthesiology, 68(6), 540. https://www.ncbi.nlm.nih.gov/pmc/articles/pmc4667138/

T-Test Application and Interpretation ﻿
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