Is it appropriate to do nonparametric and parametric significance testing on clustered data?
More precisely, I performed clustering on PCA dimension reduced data, and created cluster labels for data points using the usual procedure with standardization, parameter sampling, elbow... The clusters were created over the PCA reduced data (call it dataset 'X-hat')---reduced from a couple hundred features (call the original dataset 'X') to around 30 projected features. Is it fair to apply the usual statistical analysis techniques like anova, logistic regression, pairwise t-tests... over dataset X?
I'm not sure how else to explain these clusters. All of my coursework never discussed this, and idk what books/papers to take a look at.
Addendum: I'm hoping to achieve some inferential explainability as to why these clusters are formed particularly in the preimage of the PCA process where we have all the features defined in their "original condition" directly corresponding to the distribution of each feature.