I have two datasets consisted of 4 columns. I have to measure if the differences among the data (each column represent another data) are statistically significant. The problem is that:
- in the first dataset we have 4 columns of errors commited by estimators (measured in milliseconds- delay of premature estimation). Each row is representing errors for one signal and each column represents different estimator (algorithm). Problem: the data (columns) are not normally distributed, they are usually skewed to the left side and some of them have big outliers and they have different standard deviations (first column has 5x smaller std. than the column which has the biggest std). Which test do I use in this case?
- in the second case the dataset consists of 4 rankings represented in 4 columns and 10 rows (players). Each row consist of the score that player earned in particular ranking. There are some differences among these rankings for example- player who has been on the 7th place in the 1st ranking has fallen down onto 9th place in the second ranking (and so on). I want to confirm that these differences are statistically significant. According to KStest it is not normally distributed and again- the differences in standard deviations is large. Which test do I use in this case?