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Write a minimum 175 to 200 word response essay on Chapters 11 and 12 reviewed the t-test, a statistical method for comparing summary scores between two samples. First, when would an independent t-test be used for such comparisons? Second, when would it be more appropriate to use a dependent t-test? Finally, based on your readings, in your view, is the magnitude of an effect size as/more/less important in comparison to statistical significance?
Use research methods design and analysis by Christensen 11th or 12 edition & Statistics for people who think they hate statistics by Salkind. 3edition
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There are three type of t-test, one-sample t-test, independent sample t-test, or dependent sample t-test. A t-test is a statistical test that determines whether there is a statistically significant difference between two unrelated groups. A dependent t-test can be used to test either a change or difference between two related groups, but not both at the same time. Effect size is a measure of how different two groups are from one another. Effect size gives us an idea about the relative positions of one group to another
Write a 75 word response to this discussion below
An independent t-test can be used when you have two independent samples, different people are in each group. When the researcher is interested in finding out the difference (if any) between the two groups-independent of each other (Salkind, 2010). In an independent study, each participant is only tested once. When you have a dependent t-test the participants are measured more than once, rather than separate like the independent t-test. Dependent t-test samples are directly paired with observation in the other sample.
The magnitude of an effect size is more important in comparison than the statistical significance in my opinion. There can be a statistical significance but is the difference you observe really the difference. This can be found through the effect size. Effect size will tell us how big the difference is and the magnitude of the treatment (Salkin, 2010). Both give us something to compare to gain insight as to the real difference and significance.
Salkind, N. (2013). Statistics for People Who (Think They) Hate Statistics. Thousand Oaks, California: Sage Publications, Inc.