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.
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.