One-Sample T-Test (SPSS)

In further exploring the utilization of the various built in functions of SPSS, today we will be assessing the usage of the One-Sample T-Test.

A One-Sample T-Test measures the significance of a sample data set’s mean against the known, or assumed, mean of a population.

Example:

A high school gym instructor measures how many push-ups each individual student can perform on the school’s intramural day. His results are as follows:

Is the mean of the set, assuming an alpha of .05, significantly different from the national average of push-ups by student (18)?

To calculate this data is SPSS, first choose “Analyze” from the top menu, then choose “Compare Means”, and finally, select “One-Sample T Test”.

Performing the previous tasks should bring up the menu below. “Test Variable(s)” will be the variable set that you wish to analyze, “Options” will allow you to change the confidence interval percentage. Since our alpha is .05, we will leave the “Confidence Interval Percentage” at 95%.

This produces the output:

Our hypothesis test for this scenario is:

H0: µ = x (The sample mean is equal to the population mean)

H1: µ ≠ x (The sample mean is not equal to the population mean)

Since we are looking for general differentiation, our test will be two tailed.

With a p value of .166, we cannot reject the null hypothesis, and therefore, can assume that the sample mean does not significantly differ from the population mean.