Course Content
Orientation, introduction to the course
1. Human-Robot Interaction (HRI)
2. Research Methods in Human-Robot Interaction
3. Smart Cities & HRI
The demand for city living is already high, and it appears that this trend will continue. According to the United Nations World Cities Report, by 2050, more than 70% of the world's population will be living and working in cities — one of many reports predicting that cities will play an important role in our future (UN-Habitat, 2022). Thus, as cities are growing in size and scope, it is shaped into complex urban landscape where things, data, and people interact with each other. Everything and everyone has become so connected that Wifi too often fails to meet digital needs, online orders don't arrive fast enough, traffic jams still clog the roads and environmental pollution still weighs on cities. New technologies, technical intelligence, and robots can contribute to the direction of finding solutions to ever-increasing problems and assist the evolution of the growing urban space.
Human-Robot Interaction
About Lesson

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. 

Dix, A. (2020). Statistics for HCI: Making Sense of Quantitative Data. Morgan & Claypool Publishers.
Robertson, J., & Kaptein, M. (2016). An introduction to modern statistical methods in HCI (pp. 1-14). Springer International Publishing.
Larson-Hall, J. (2015). A guide to doing statistics in second language research using SPSS and R. Routledge.
Aldrich, J. O. (2018). Using IBM SPSS statistics: An interactive hands-on approach. Sage Publications.
Salcedo, J., & McCormick, K. (2020). SPSS statistics for dummies. John Wiley & Sons.