I have got a question that
What are the differences of the Student's T-Test, Kolmogorov-Smirnov Test (KS-Test), and the Mann-Whitney-Wilcoxon Test (MWW-Test)?
In the case of the T-test, the null hypothesis is μ1 = μ2, indicating that the mean of feature values for class 1 is the same as the mean of the feature values for class 2. In the case of the KS-test, the null hypothesis is cdf(1) = cdf(2), meaning that feature values from both classes have an identical cumulative distribution. Both tests determine if the observed differences are statistically significant and return a score representing the probability that the null hypothesis is true (From Levner 2005).
How about the MWW-Test? Are there any assumptions of the distribution of the data for these tests?
When we want to test if there is significant difference between paired variables, which one or ones we should use?
Please also provide reference papers/links when you answer the questions.