T-tests allow you to determine whether differences in the averages of two different samples are statistically significant, indicating actual differences between the two samples, or if the differences happened merely as a result of error. Once you have your t-values, you need to know how to interpret them. Interpretation requires a standard table of significance, available in the back of most statistics textbooks.
Set a "risk level" for the data. This level, also known as the alpha level, refers to the chance that differences between your samples will appear to be statistically significant, after conducting a t-test, even if they aren't statistically significant. Typically, the alpha level is set to 0.05.
Calculate how many degrees of freedom you have in your data. Degrees of freedom consist of the total number of elements in both samples, minus two. For instance, if both of your samples contained 36 elements, the degrees of freedom would be 36+36-2=70.
Refer to your table of significance and use the alpha value and the value for degrees of significance to find the t-value listed in the table. If your t-value is greater than the t-value listed in the table, then you can conclude that the differences between your two samples are statistically significant. If your t-value is less than the t-value listed in the table, then differences between your samples are not statistically significant for the alpha value that you chose.