There really is no reason for me to read the paper closely. They say that the null hypothesis is wrong, and they know exactly why. Then, like multiple people re

There really is no reason for me to read the paper closely. They say that the null hypothesis is wrong, and they know exactly why. Then, like multiple people responding to me here, they also want to say somehow the null hypothesis is true. Everyone who has questioned me also does admit there is something wrong with that hypothesis they tested. You do it too: "the software is not calculating the null hypothesis that users expect", but then you also want to say the null hypothesis they tested is true! Just bizarre, what underlying confusion is making people repeat something clearly incorrect? The null hypothesis cannot be both true and false at the same time. There is a big difference between a "positive" result that is a "false positive" and a "positive" result due to a false null hypothesis. This is a clear cut case of the second.