Topic: Discuss the pros and cons of each technique. Pros and cons in using a different two-sample t-test
1) reply to the following discussions using 2 references for EACH discussion
2) the replies should be 1 main paragraph
Anna
A T-Test is a hypothesis testing tool used to test an assumption of a given population. It is a type of inferential statistics used to determine the significant difference between the means of two groups with similar features. The samples are compared based on their means and is very easy to compare samples of independent groups.
Pros: Essential for generalization, the results achieved after the t-test are useful for concluding if they are correct, they can be applied to the entire population. Easy to calculate: It is easy to calculate data from two samples with the aid of a computer. Standard software programs that support statistical functions like Microsoft Excel can be used to calculate t-test data. Easy to gather data: A small number of subjects for independent t-test samples are required. Only one value from each subject is needed; it requires values of subjects from the two sample groups on a quantitative variable.
Cons: Difficult to find subjects: Getting the subjects for the sample data is very difficult and a very expensive part of the research process. Carry-over effects: When relying on paired sample t-tests, there are problems associated with repeated measures instead of differences between group designs and this leads to carry-over effects. Carry-over effects: When relying on paired sample t-tests, there are problems associated with repeated measures instead of differences between group designs and this leads to carry-over effects.
Charline
I agree to the sample t-test is simple to understand and interpret. The t-test formula for independent samples is easy to understand. This makes it easy to know what is going on without needing much statistical training. The paired sample t-test, sometimes called the dependent sample t-test, is a statistical procedure used to determine whether the mean difference between two sets of observations is zero. In a paired sample t-test, each subject or entity is measured twice, resulting in pairs of observations. Common applications of the paired sample t-test include case-control studies or repeated-measures designs.
Juliana
The t-test is used when comparing two populations. When completing this test the following things must be present: simple random sampling, small sample sizes, two samples are ollected from the same individuals or objects, differences are calculated, the differences create the sample that is used for the test, and either the matched pairs have differences that come from a population that is normal or the amount of differences is large enough so that the distribution of the sample mean of differences are approximately normal (Illowsky & Dean, 2018). This test is a hypthesis testing tool used to create assumptions about a population. This type of inferential statistics is used to determine the significant difference between the means of two groups that have similar features (Pros and cons of t-test, 2019). As with any test there are pros and cons. Some of the pros to using this test includes the ability to make conclusions about a group after getting results that are correct. The results are also easy to interpret, indicating how different the mean of one group is to the mean of the other group, and shows the average difference between the two. The larger the t-score the more differences are noted between the two groups and a smaller the t-score demonstrates the similarities between the two groups (Pros and cons of t-test, 2019). The t-test is also fairly robust since there are two samples from a population and these have unequal variances. It is also easy to gather data and make calculations for this test (Pros and cons of t-test, 2019). The cons to using this test include carry-over effects. when relying on paired sample t-tests there are problems with repeated measures instead of differences between group designs. The t-test also shows the differences but can’t control environmental effects but it does have an affect on the output of the test. This type of test can’t be used for making multiple comparisons because it would result in type I errors (Pros and cons of t-test, 2019).
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