Dependent T-test is performed in order to evaluate if there exists a statistically significant difference between the means of two samples that are dependent on each other (Ramachandran & Tsokos, 2014). The hypothesis tested is such that; H_0:μ_1-μ_2=0 versus the alternative hypothesis, H_1:μ_1-μ_2≠0. The SPSS output gives three tables.
The paired samples statistics give univariate descriptive statistics. In our case the sample size is 25, the mean score for the test before the statistics and the test after the statistics course are 6.32 and 7.52 respectively. The standard deviation and the standard error are 1.725, 0.345 and 1.828, 0.366 for respectively. The paired sample correlation tables give more information on the relationship between the two samples. The correlation coefficient, in this case, is 0.051indicating a weak positive correlation between the scores before and after the statistics course.
Delegate your assignment to our experts and they will do the rest.
The last table gives the test of hypothesis results (Yockey, 2017). From the table, the mean difference between the two test scores is -1.2 which implies that the test scores after the statistics course are on average 1.2 higher than the tests scores before the course. The standard deviation of the differences is 2.449, and the standard error of the mean is 0.490. The 95% confidence interval for the mean difference is (-2.211, -1.189). The last column indicates a p-value of 0.022. The p-value is less than 0.05 (statistically significant) which means we reject the null hypothesis and conclude that there is sufficient evidence at 95% confidence level to support the claim that there is a statistically significant difference between the test scores before and after a statistics course.
References
Ramachandran, K. M., & Tsokos, C. P. (2014). Mathematical statistics with applications in R . Elsevier.
Yockey, R. D. (2017). SPSS demystified . Taylor & Francis.
