1. Non-parametric tests

2. What does non-parametric mean?
Parametric tests are based on the parameters of a probability distribution: e.g., mean & variance for a normal distribution
Non-parametric tests do not make any assumptions about the underlying probability distribution.

4. Given the CLT*, why consider a non-parametric test?
If your measurement is very skewed (not normally distributed) and is better represented by the median (e.g., housing prices, income)
If you have a small sample size (n<30 for a one-mean test, n<15 a two-mean test)
If you have ordinal data, ranked data, or outliers (extreme values) that you can’t remove
CLT: Central Limit Theorem

5. Non-parametric tests in JMP: Paired responses
For paired measurements in 2 columns:
Wilcoxon Signed Rank Test (Equivalent)
Analyze>Specialized Modeling>Matched Pairs (insert the 2 matched measurements)
Alternatively: Calculate the difference between the 2 measurements and use the Distributions platform to test that the mean of the difference is different from 0
For paired measurements in 1 column with 2 rows per ID
Use the Split option in the Tables menu to split the column of measurements into two columns. 

6. Non-parametric tests in JMP: Unpaired responses
To compare 2 groups
Mann-Whitney Test/Wilcoxon Test (Equivalent)
Analyze>Fit Y by X where X has 2 categories and Y is continuous

7. Non-parametric tests in JMP: Unmatched groups
To compare >2 groups
Kruskal-Wallis/Wilcoxon Test (Equivalent)
Analyze>Fit Y by X where X has >2 categories and Y is continuous