Quantitative Results Show Statistical Analysis Independent sample t-test is a statistical technique that is used to analyze the mean comparison of two independent groups. In independent samples t-test, when we take two samples from the same population, then the mean of the two samples may be identical. But when samples are taken from two different populations, then the mean of the sample may differ. In this case, it is used to draw conclusions about the means of two populations, and used to tell whether or not they are similar. Discover How We Assist to Edit Your Dissertation ChaptersAligning theoretical framework, gathering articles, synthesizing gaps, articulating a clear methodology and data plan, and writing about the theoretical and practical implications of your research are part of our comprehensive dissertation editing services.
Assumptions
in independent samples t-test:1. Assumes that the dependent variable is normally distributed. 2. Calculate the standard deviation for the independent sample t-test by using this formula: 3.
Calculate the value of the independent sample t-test by using this formula: 4. Degree of freedom for independent sample
t-test: Where 5. Hypothesis testing: In hypothesis testing for the independent sample t-test, statistical decisions are made about whether or not the two population means are identical. Compare the calculated value of the independent sample t-test with the table value of the sample t-test. If the calculated value of the independent sample t-test is greater than the table value of the predetermined significance level, we will reject the null hypothesis and say that the means of the two groups are different. If the calculated value of the independent sample t-test is less than the table value, then we will say that the means of the two groups are the same. Independent sample t-test and SPSS: Most statistical software has the option to perform the independent sample t-test. In SPSS, to perform the independent sample t-test we have to perform following procedure: 1. Click on the “SPSS 16” icon from the start menu. Select the grouping variable and insert them into the grouping variable box. Define the group in the independent samples t-test. Select the dependent variable and insert them into the test variable list. Click on “option” and select “% confidence interval.” As we click on the “ok” button, the result window for the independent sample t-test will appear in front of us. Independent sample t-test group statistics table: This table will show the total number in each group, the mean, the standard deviation and the standard error for the independent sample
t-test. Independent sample t-test statistics table: In this table, the first test will be Levene’s test, which is used to test the assumptions of equal variance between all the groups in the independent sample t-test. Significance value of F is used to make the statistical decision about the assumptions of equal variance. The second test statistics will show the calculated value of the independent sample t-test, the degree of freedom, and the significance value of the independent sample t-test. This significance value is used to make the statistical decision about the mean of the two groups. If the calculated value is less than the predetermined significance level, then we can say that the means are significantly different. What does it mean if you reject the null hypothesis?After a performing a test, scientists can: Reject the null hypothesis (meaning there is a definite, consequential relationship between the two phenomena), or. Fail to reject the null hypothesis (meaning the test has not identified a consequential relationship between the two phenomena)
What does it mean when we reject the null in a one sample tThe test statistic is more extreme than the critical t values; in other words, the test statistic is less than -2.042, or is greater than +2.042. You reject the null hypothesis that the mean is equal to the specified value.
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