Week 6 began statistical analysis using SPSS, specifically for nonparametric tests. Nonparametric data can be described as data that does not conform to normal distribution. A simple example is ranked data such as movie reviews (0 – 5 stars). A major limitation of nonparametric data is the increased sample size required to gain sufficient significance to reject a null hypothesis.
A good summary of the assorted types of nonparametric tests was found at http://www.graphpad.com/www/book/choose.htm:

Type of Data 
Goal 
Measurement (from Gaussian Population) 
Rank, Score, or Measurement (from Non Gaussian Population) 
Binomial
(Two Possible Outcomes) 
Survival Time 
Describe one group 
Mean, SD 
Median, interquartile range 
Proportion 
Kaplan Meier survival curve 
Compare one group to a hypothetical value 
Onesample t test 
Wilcoxon test 
Chisquare
or
Binomial test ** 

Compare two unpaired groups 
Unpaired t test 
MannWhitney test 
Fisher’s test
(chisquare for large samples) 
Logrank test or MantelHaenszel* 
Compare two paired groups 
Paired t test 
Wilcoxon test 
McNemar’s test 
Conditional proportional hazards regression* 
Compare three or more unmatched groups 
Oneway ANOVA 
KruskalWallis test 
Chisquare test 
Cox proportional hazard regression** 
Compare three or more matched groups 
Repeatedmeasures ANOVA 
Friedman test 
Cochrane Q** 
Conditional proportional hazards regression** 
Quantify association between two variables 
Pearson correlation 
Spearman correlation 
Contingency coefficients** 

Predict value from another measured variable 
Simple linear regression
or
Nonlinear regression 
Nonparametric regression** 
Simple logistic regression* 
Cox proportional hazard regression* 
Predict value from several measured or binomial variables 
Multiple linear regression*
or
Multiple nonlinear regression** 

Multiple logistic regression* 
Cox proportional hazard regression* 
All of the tests described in the table above can be applied via SPSS. Note that “Gaussian population” refers to normally distributed data. Not featured in the table above is the sign test, perhaps as it is described as lacking statistical power of paired ttests or the Wilcoxon test.
One question that immediately comes to mind is how the process of normalization can be applied to force comparison of normally distributed data to nonparameter data.
The lecture went on to describe important assumptions and the rationale behind several test methods. I will await further practical testing with SPSS before going into more detail on them.