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?
I think op is refering to hypothesis tests between baseline. What’s the point in reporting variance and standard deviation? My outputs on regression tasks are always non-normal. I tend to always plot the cumulative frequency but assigning a number to the distribution such as the variance will have very little meaning.