One Sample t Test
The one-sample t-test is used when we want to know whether our sample comes from a particular population, but we do not have full population information available to us. For example, we can use one sample t test for comparing the mean of our data set against a known mean (e.g., we know from various previous measurements that our mean SUS score for our system was X, but after some changes in the UI, using a sample of users we measured a SUS score of Y, and we want to examine if this change is statistically significant).
The one-sample t test (or single sample t test) determines whether the sample mean is statistically different from a known or hypothesized population mean. The one Sample t test is a parametric test, which means that we assume that our population follows a normal distribution. The assumptions for using this test are:
- The dependent variable should be measured at the interval or ratio level (i.e., continuous).
- The data should be independent (i.e., not correlated/related), which means that there is no relationship between the observations.
- There should be no significant outliers.
- The dependent variable should be approximately normally distributed.
For further reading about one sample t test using SPSS, you may read this:
Lazar, J. , Feng, J. H., Hochheiser, H. (2017), Research methods in human-computer interaction: Morgan Kaufmann, 2017.
Sauro, J., and Lewis, J. R., (2016). Quantifying the user experience: Practical statistics for user research: Morgan Kaufmann.