Steps to perform a t-test
In cases were the two groups being compared are not connected in any way, use the regular t-test. The case of the two groups of data collected at different sites on a river is an example where a regular t-test would be used. The data sets are independent of each other.
Next, consider the case where a group of students takes a pre-test, is given some form of instruction and then takes a post-test to see if the instruction was effective. The two sets of test data are connected since the same students provided both sets; each student has a pre and a post test! In this example a paired t-test would be used.
The alpha level you choose determines the risk you are willing to take of being wrong when you reject H0. Remember, if you reject the null hypothesis, it means the difference between the group means is so large it would rarely be found between two means from populations with the same distribution...but it might be found! Setting the alpha level at 0.05 means you are willing to accept the risk of being wrong 5 times out of 100. Therefore, if you reject the null hypothesis, it means you could be wrong, but the mean difference between the t-test groups would only be found between groups from populations with the same distribution 5 times out of 100. The most common alpha level used by researchers is 0.05. However, if you cannot afford to be wrong five times out of 100, set the alpha level to 0.01 and you risk being wrong only 1 time out of 100 when you reject H0! Of course to reject the null hypothesis at the 0.01 alpha levelwill require a larger difference between the group means.
Generally, researchers select two-tailed tests because they are not sure whether the difference between the means being compared will be positive or negative. In cases where the researcher has done some previous research and is sure the difference between the means will be either positive or negative, a one-tailed test can be used. However, there is a greater chance of being wrong if a one-tailed test is used so the researcher really needs to know a lot about the data he or she is studying!
The t-critical is a number that represents the size of the mean difference required for the alpha level selected. The t-statistic is a number that represents the actual size of the difference between the two test means. Let's assume α =0.05. If t-statistic> t-critical is true, it means that the difference between the two test means is so large it would only be found less than 5 times out of 100 in means coming from populations with the same distribution. Therefore, there is a 95% chance that the means come from different populations and the null hypothesis should be rejected. The p is the "calculated α." It represents the probability of the difference in the test means coming from the same population. Therefore, if p=0.04 (less than α), it means that the difference between the two test means is so large it would only be found 4 times out of 100 with means coming from populations with the same distribution and the null hypothesis should be rejected. Conversely, what if p=0.10 (greater than α)? In this case, the difference between the two test means would be found 10 times out of 100 with means coming from populations with the same distribution. ! Since α =0.05, the 10% chance is greater than the 5% chance allowed and the null hypothesis should be retained.
Copyright © 1997 Central Virginia Governor's School for Science and Technology Lynchburg, VA