Some important notes for T-tests
In social research, there are certain data which are collected and have to be found whether they are significantly different from one another. The T-test establishes the significant dissimilarities between two sets of data and brings out that they are not related to one another. If there is only one set of data drawn from a sample, the T-test done on that sample gives a single set of values and is called single T-test, while if the same sample is being used for comparing several criteria and different characteristics of the same sample is tested, there will be several T-tests performed on the same sample or population. Then, this type of T-test can be easily plotted in graphical form and is called T-sampling distribution.
What are difficulties encountered in T-tests problems?
Depending on the sample size, different types of T-tests exist, which should be known to the students prior to conducting any type of T-test. The degrees of freedom for T-distribution should be understood well and the students should be evident of the advantages of using it, as even small sample size is sufficient for performing the T-test. Using the values of T-tests, students should be able to calculate the probability of samples. One more concept which students should be aware of is that T-tests are usually performed on normal distribution samples, so skewed data cannot be handled by T-tests. Therefore, accurate knowledge on T-test applications and the way to calculate T-tests for data obtained from samples are very essential for students to gain proper comprehension of the T-test.
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