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

Types of Error in Hypothesis Testing

According to Lazar, Feng, and Hochheiser (Lazar, 2017), significance testing is a technical process that involves contrasting a null hypothesis (H0) with an alternative hypothesis (H1) to determine the probability of the null hypothesis being true. However, all significance tests are at risk of Type I and Type II errors. Type I errors, also known as “false positives” or α errors, occur when the null hypothesis is mistakenly rejected despite being true. Conversely, Type II errors, also known as “false negatives” or β errors, occur when the null hypothesis is not rejected even though it is false (Rosenthal and Rosnow, 2008). 

In general, Type I errors are considered more severe than Type II errors. Statisticians refer to Type I errors as “gullibility” mistakes, as they may lead to a situation worse than the current one. For instance, if a new medication is erroneously believed to be more effective than the current medication, patients may switch to an inferior medication. On the other hand, Type II errors are considered “blindness” mistakes that may result in missed opportunities to improve the current situation. In the medication example, a Type II error would mean that the test fails to identify the new medication as more effective than the current treatment, causing patients to miss out on a better treatment. 


For further studying about type I and type II errors watch this video:  


Lazar, J. , Feng, J. H., Hochheiser, H.  (2017), Research methods in human-computer interaction: Morgan Kaufmann, 2017. 

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.