The final lecture on quantitative data analysis covered4 specific statistical test:

  • Binomial – Given a weighted coin, how many heads will probably result from 30 tosses
  • Median – Checks that the medians of two populations are not significantly different
  • Mood’s median test – Checks for significant similarity between unrelated samples (non-parametric)
  • Kilmogorov-Smirnov – Measure the cumulative difference between data, are the data sets different?
  • Friedman – Testing for significant differences across testing intervals on a sample population
The lecture slides included clear examples of these tests. The tutorial followed up with some practical examples using SPSS. After the 4 weeks of quantitative data analysis we now have a decent toolbox specifically for non-parametric data analysis. Our assignment requires application of these tools. I imagine that the assignment will give lease to some of the ambiguities that arise when reasoning from quantitative analysis.
non-parametric
An example of non-parametric data (source: http://perclass.com/doc/kb/15.html)