In the test for equivalence (or non-inferiority / non-superiority) we define a "minimal effect size" $d$, and test that the measured effect is not larger that $d$
$$X_1 \sim PDF1$$ $$X_2 \sim PDF2$$ $$TOST: mean(X1 - X2) < |d|$$
The difference from a t-test being that we do assume an effect size $> 0$, but we set a boundary under which to neglect this effect size.
However, in the TOST procedure, the null hypothesis claims that the effect size is larger that $d$, and the alternative hypothesis is that it is smaller. This is a bit against the usual null hypothesis that assumes randomness, non-relatedness, and absent effect sizes.
If we assume that the treatments that generate $X_1$ and $X_2$ are meaningless, and both are random variables drawn from the same distribution $PDF1 = PDF2$, which is what we would assume doing a t-test, the null of the TOST should also be the absence of an effect (i.e the effect size being smaller than a pre-defined $d$).
Why does the TOST procedure have a contra-intuitive null?