# One-Sample T Test

This feature requires the Statistics Base option.

The One-Sample T Test procedure tests whether the mean of a single variable differs from a
specified constant and automates the *t*-test effect size computation.

- Examples
- 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.
- Statistics
- For each test variable: mean, standard deviation, standard error of the
mean, and the estimation of the effect size for the
*t*-test. The average difference between each data value and the hypothesized test value, a*t*test that tests that this difference is 0, and a confidence interval for this difference (you can specify the confidence level).

## Data Considerations

- Data
- To test the values of a quantitative variable against a hypothesized test value, choose a quantitative variable and enter a hypothesized test value.
- Assumptions
- This test assumes that the data are normally distributed; however, this test is fairly robust to departures from normality.

## Obtaining a One-Sample T Test

This feature requires the Statistics Base option.

- From the menus choose:
- Select one or more variables to be tested against the same hypothesized value.
- Enter a numeric test value against which each sample mean is compared.
- Optionally, you can:
- Select Estimate effect sizes to control the
estimation of the
*t*-test effect size. - Click Options to control the treatment of missing data and the level of the confidence interval.

- Select Estimate effect sizes to control the
estimation of the

This procedure pastes T-TEST command syntax.