I have a dataset of 815 positive examples and 9492 negative examples for a certain class. Each example is represented by 12 features and a target label (i.e. TRUE/FALSE). The dataset is in a CSV file and can be downloaded from here.
The 12 features are measures of different quality aspects. The question I am trying to answer is whether or not the positive instances of this class are significantly better (or worse) in any of these quality aspects compared with the negative instances. In other words, I need to know which of the features (if any) have significantly different values in the positive/negative examples.
I would deeply appreciate it if you could suggest one or more appropriate statistical analysis for this dataset.
UPDATE: a new version of the dataset with 15 features has been uploaded.
UPDATE 2: I have tried using Mann-Whitney test and I have surprisingly found statistically significant differences in 12 out of 15 features, 11 of which are significant at p-value <0.0001. This may indicate that the test used is not the appropriate one. If it is indeed an appropriate test, how can I appropriately select the 3 features with the most significant differences?
UPDATE 3: all features are on the Ratio scale.