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: 

https://statistics.laerd.com/spss-tutorials/one-sample-t-test-using-spss-statistics.php

References 

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.