In "August 2019 draft of the 3rd edition of Jurafsky & Martin Speech and Language Processing" book's section 3.5 (Kneser-Ney Smoothing) it is stated that
The astute reader may have noticed that except for the held-out counts for 0 and 1, all the other bigram counts in the held-out set could be estimated pretty well by just subtracting 0.75 from the count in the training set! Absolute discounting formalizes this intuition by subtracting a fixed (absolute) discount d from each count
The part I don't understand is that intuitively, if the two corpuses are from the same topic, then the distribution of n-gram counts had to be at least similar to each other, and if they are about different topics, the distribution had to be much different. How it comes that the distribution of counts of n-grams differs by a fixed number, and how are we guaranteed that it will always hold?