First, I have not invalidated the discipline, I have said not to trust statisticians who are clearly acting outside the bounds of the fundamental assumptions of statistics, and that statistical methods DO NOT function on adversaries. Consider the following two scenarios:

1) Alice and Bob are sending messages to each other in a lanugage I don't understand, and I, using statistical methods, wish to find out what they are saying.

2) Alice and Bob are sending messages to each other and don't want me to gain any information about this messages. I, using statistical methods, wish to find out what they are saying.

In scenario 1, I am likely to succeed. In scenario 2, the consensus is that I'm fucked. In just about every way. I can't tell what they're saying, I can't tell that any message that I have discerned is meant to mislead me, or doesn't carry some additional message hidden in the entropy of the message that was meant to mislead me. I can't tell if the communication is just noise meant to distract me, and if we want to talk practically, I can't even tell if they can communicate. Basically the only inference that I can draw is that they can't communicate faster than the speed of light.

Here's another example: Gerrymandering. The scenario is that one party has a clear advantage in terms of number of representatives vs proportion of population. We must establish whether that number was arrived at fairly, or by cheating. We assume that the party in charge of drawing the borders knows what tests we can perform, because that is always the assumption that you give to an adversary.

The adversary has a very simple (though potentially computationally expensive) algorithm to run. Check all possible border configurations for both advantage and your cheat detection. Pick the one which maximizes advantage which does not pass the cheat threshold, or just whatever your utility is.

Statistics needs the assumption of good faith in order to operate. Anyone who uses statistical methods when that assumption that cannot be made is at best a bad statistician.