I have been reading many deep learning papers. In some of them, I see the term statistical significance when they compare predicted results by models in a given dataset.
So, suppose you have two classifiers
B. You use these models to classify a dataset with 1000 samples and get the accuracies
Could you give examples when one of the models is/isn't better/worse with statistical significance?
I know that this question has to do with null hypothesis, p-value and related topics. However, I can't figure out how to relate this to a dataset and models predicting labels from the dataset.