While interpreting the correlation between ranks, should I use the rho value (for spearman method), tau value (for kendall's tau method), w value ( for kendall's w method) or should I take in consideration the p-value? And does having NaNs values ​​in the ranks impact the interpretation of the correlation?

What are the characteristics of the three correlation coefficients and what are the comparisons of each of them/assumptions? Can somebody kindly take me through the concepts?

I'm trying to compute a p-value for a two tailed test following Wikipedia formula which indicates that: one computes Z, and finds the cumulative probability for a standard normal distribution at -|Z|. For a 2-tailed test, multiply that number by two to obtain the p-value I'm using this Rust's library which computes Tau value and then you can get the significance from this source code. The problem is that this calculator (with default values) gives a 2-sided p-value = 0.0389842391014099. …

I have a dataset of 45 non-linear numerical values and 2 categorical values. I am making a feature selection to predict categorical variables one by one or together. I used the correlation ratio and kendall rank correlation coefficient to calculate the strenght of the relations. Among these two methods, which one should I use as the primary method to sort the strenght of variables? For example the table below is sorted by correlation ratio. Should I sort the table with …