I agree. More succinctly, it confounds two types of results: a null result due to statistical noise (big error bars, experiment failure) and a null result where the null model is more likely (actually, the effect doesnt exist).

Like many things in statistics, this is solved by Bayesian analysis: instead of asking if we can reject the null hypothesis, the question should be which model is more likely, the null model or the alternate model.