Often when I read ML papers the authors compare their results against a benchmark (e.g. using RMSE, accuracy, …) and say “our results improved with our new method by X%”. Nobody makes a significance test if the new method Y outperforms benchmark Z. Is there a reason why? Especially when you break your results down e.g. to the anaylsis of certain classes in object classification this seems important for me. Or do I overlook something?
Most ML work is oriented towards industry, which in turns oriented towards customers. Most users don’t understand p values. But they do understand one number being bigger than another. Add on that checking p values would probably remove 75% of research publications, there’s just no incentive.