 Dear students now we are going to study one sample T test. One sample T test is somehow different from the paired sample T test and independent sample T test. One sample T test is conducted when you are going to compare your sample mean with the population mean or in some cases if you do not have the population mean then you are going to compare your sample mean with the hypothesized population mean. So you deduce based on this test that your sample mean is statistically significantly differing from the mean of the population or it is statistically significantly different from the mean of the population. If we talk about assumptions then this test is conducted when you have a composite score i.e. your variable continues in nature. There are no outliers in that and the data is normally distributed. If we talk about its hypothesis then we can state its null hypothesis that there is no mean difference between computed and test value of the variable. And there is statistically a significant mean difference between computed and test value of the variable. Now here I will give you an example that in the year 2020 the students passing out their average GPA is different from the students of the Department of Gender Studies with the average GPA of previous sessions. So how do we conduct this test? We will go to the analyzes, we will go to the compare means, we will select one sample T test. Here we have selected the GPA and based on the previous data the test value is 3.8 which is the mean of your population. Now when we conduct this test we will have these two tables. One of them is the mean of your sample which is 3.23. The test value of 3.8 is very less. Because of this your SIG2T is less than 0.05. It means that we are going to accept the H1 that there is a statistically significant mean difference in the computed value and test value. This is the average score of this session. It is comparatively less than the scores of previous sessions. This means that X bar is not equal to mu. So after conducting one sample T test we have come to know the standard average mean difference and confidence intervals. I have explained in the lecture of independent sample T test how we compute this. So now we will do an exercise in this next module.