 Now I am going to demonstrate t-test in SPSS. After opening the data in SPSS, we need to go to Analyze. So all the three types of independent tests as to be done by clicking on Analyze, by keeping the cursor on compare means, we will get all the three types, one sample t-test, independent t-test and paired t-test. First we will see how to do one sample t-test. So now I am clicking on one sample t-test, I am going to check whether the age of my sample is significantly different with the population average or mean. Suppose if I want to check whether this average of age among this sample is significantly different from the population mean which I know from the data. So when I put this 45 here, suppose randomly I am putting 45 here, then I click on OK. So the one sample t-test will appear here. So in our sample, after 120, the mean age was 39.5, standard deviation of 9.4 and standard error of mean is 0.85. So this when compared with the test value of 45, we have a t-value of minus 0.6387. So significance is 0.001 which is significantly different when compared to this 45. So that here the p-value is this significant two-tailed. With the 95% confidence interval of this mean difference is minus 7.12 minus 3.78. So now I am going to demonstrate the next independent sample t-test. Go to analyze, compare means, click on independent t-test. Suppose if I want to compare the age between two different gender that is male and female, it will ask for the grouping variable we need to define. Here the male and female has been given as m and f. So here for independent t-test, the objective is to compare any numerical variable between two groups, two independent groups. So male and female, so I am clicking on OK. Among the male, the mean is 39.5. Among the female, the mean of age is 39.4. So when we look at this table, the interpretation, we need to look at this equal variances assumed by default when we are doing independent t-test, leaving test for equality of variances. When this is not significant, then we can go with the first row. When it is significant, we have to go with the second row. So here it is not significant. So we need to take the first row that is the t value is 0.07 with the degree of freedom 118 and significance is 0.94. So that is the interpretation of independent t-test that is the average or mean age is not significantly different between male and female. Now we are going to see about paired t-test, same analysis, compare means again paired t-test. So we need to click on two paired variables. That is here I am clicking the baseline pulse and the pulse rate at 10 minutes. So this will be considered as a paired observation. So we need to use paired t-test to compare these numerical variables between repeated observations. So when we look at the t-test here, so baseline the mean is 74, the pulse rate at 10 minutes is 76.4. That is a slight increase of nearly two pulse rate at 10 minutes. We need to look at whether this is a significant difference or not. So we need to look at this p-value here it is 0.018 which is less than 0.05, hence it is significant. So this increase of pulse rate of 2 from 74 to 76 can be called as significantly different between these two groups. So that is the interpretation of paired sample t-test. So the basic about doing t-test in SPSS is go to analyze, click on compare means, we will get all the three type of t-test which we can do for comparison of mean or average between two groups and this one way ANOVA will be for more than two groups.