 So how we do t-test and calculate the t-value using SPSS. SPSS is not that hard, you don't need to manually calculate that. You just enter the data, find the right test and then you just hit click button. Here's an example and I'll show you physically also like how we actually do it in our next module. But right now, let's see, first let's do a random sample of 5 people with scores on a test. 11, 13, 4, 12 and 10 is obtained from a population with a mean of 13. So the population's mean is 13, whereas the 5 samples we collected are from the same population. Their score is this. And this score is after treatment. Our searcher wants to examine if the sample mean differs from the population mean after the treatment. So we enter all the values in one column in SPSS. You know how we started the SPSS? How do we enter the data? So we enter the data, then we go to the menus. We have an Analyze menu here. We will go to Analyze and after going to Analyze we will go to Compare Means and we will click on one sample t-test on Compare Means. After clicking on one sample t-test, we will get a dialogue box like this. So here was our variable, which we sent to the test variable from arrow key. So we move this scores variable from here to here. Then in the test value box, we have a test value that we have to test against the value of which population. And they gave us the test value of the population, it was 13. So we will add 13 in it. So we added 13 and then we will add addition to perform the hypothesis test. The program computer confidence interval as well. So you enter one sample t-test in it, your confidence interval by default is 95%. You will let it remain the same and you will hit OK button. When you will hit OK button, it will open the output file like this. So the interpretation of this output file is important. So first of all, it gives you a descriptive that our five values are the main 10. Their standard deviation is 3.53 and its standard error of mean. So remember that we calculated it under s over n. You don't need to calculate that. But you should understand what this standard error is. What this standard deviation is, this is ss over n under root. n minus 1, this value. And this is the mean that we have given you. Now this is the value of t-test which is this one. The value of t-test is minus 1.89. I have told you that by default, if you calculate t, it is 2-tailed. So it assumes that the value can be more or less. So in t-test, in our SPSS, the alternative is that mu is not equal to 13. So the value of t-test is minus 1.89. So this value of t means that your significance value is 0.13 and we have to compare it with 0.05. So comparing it with 0.05 means that this value should always be less than 0.05. If this is smaller than 0.05, it means that results are significant. And if the results are significant, we will reject the null hypothesis. But in this case, the value of p is called p-value. So p-value that is greater than 0.05. So 0.13 is greater than 0.05, which means that our value is not significant. And if the value is not significant, we fail to reject the null hypothesis. That population mean is 13. So actually we will keep this null hypothesis because it is not significant. And the value of t is smaller. The value of p is greater than 0.05. So we will do the same as we did manually. But with just one click, you can find out the t-value. You can find the p-value. You can find standard error, standard deviation, everything. But you should know what these values are, how you're going to understand them and how you're going to interpret them. So when you report this value in this, you will write that t is called talus size. You have to give degrees of freedom because our five values are c. So 5 minus 1, 4. So t degrees of freedom is equal to minus 1.89. And you will write p is equal to 0.13. And then you will write down that the results are not significant. So we fail to reject the null hypothesis. This means that the mean of the sample population is not significant.