Purpose

     The t-test for a single mean allows us to test hypothesis about the population mean when our sample size is small and/or when we do not know the variance of the sampled population. In so-called one-sample t-tests, the observed mean (from a single sample) is compared to an expected (or reference) mean of the population (e.g., some theoretical mean), and the variation in the population is estimated based on the variation in the observed sample.
Example: A researcher might want to test whether the average IQ score for a group of students differs from 100. Or, a cereal manufacturer can take a sample of boxes from the production line and check whether the mean weight of the samples differs from 1.3 pounds at the 95% confidence level.
    This test assumes that the data are normally distributed; however, this test is fairly robust to departures from normality. To check it use Normality Tests. See also Why the "Normal distribution" is important.

Preparations

To run this procedure, select a range, and then run the Statistics→Basic Statistics and Tables→One Sample T-Test... command.
 

Example: TTest2.st

Results