Non-Parametric Tests

Compared to parametric tests, nonparametric methods make fewer assumptions about the data. Although nonparametric tests are also called “assumption-free” tests, it should be noted that they are not actually free of assumptions. Non-parametric analysis collapses the data into ranks so all that matters is the order of the data while the distance information between the data points is lost. Therefore, nonparametric analysis sacrifices the power to use all available information to reject a false null hypothesis in exchange for less strict assumptions about the data (Lazar, 2017). 

We will examine two non-parametric tests, the Chi-square test, and the Mann-Whitney U test. 

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. 

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.