HOW TO RUN
ONE SAMPLE T TEST
IN SPSS

Using One Simple T-Test Statistic in Research

This easy tutorial will show you how to run One Simple T-test in SPSS, and how to interpret the result. In another word, The aim of this commentary is to overview checking for student t-test in statistical analysis using SPSS.

Use Student’s t-test for one sample when you have one measurement variable and a theoretical expectation of what the mean should be under the null hypothesis. It tests whether the mean of the measurement variable is different from the null expectation (Source).

One-Sample t-test is used when we want to know if our sample comes from a certain population. Still, we do not have complete information about that population, that is, we want to compare whether our results match the results obtained for a specific population. Therefore, we need to have one variable (for example, Math test score) and to know the average value of Math test scores in the population.

Assumptions

This section describes the assumptions that are made when you use one of these tests. The key assumption relates to normality or the nonnormality of the data. One of the reasons for the popularity of the t-test is its robustness in the face of assumption violation. However, if an assumption is not met even approximately, the significance levels and the power of the t-test are invalidated. Unfortunately, in practice, it often happens that more than one assumption is not met. Hence, take the steps to check the assumptions before you make important decisions based on these tests. There are reports in this procedure that permit you to examine the assumptions, both visually and through assumption tests.

One-Sample T-Test Assumptions

The assumptions of the one-sample t-test are:
1. The data are continuous (not discrete).
2. The data follow the normal probability distribution.
3. The sample is a simple random sample from

An Example: One Simple T-Test in SPSS

One sample t-test was conducted to determine whether there is a difference between the results on the Math test and the true population mean (M=86.00). The results indicate a significant difference between the true mean (M=86.00) and the mean Math test score (M=73.08; SD=16.89), [t(36) = -4.651, p = .000]. We, therefore, reject the null hypothesis that there is not a difference between the true mean and the comparison value and conclude that our mean Math test score is significantly different from the true population mean.

Visit our Reporting One-Sample t Test Page for more details.