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:

To calculate this data is SPSS, first choose

**“Analyze”**from the top menu, then choose

**“Compare Means”**, and finally, select

**“One-Sample T Test”**.

**“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:

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.

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