The author seems to go completely astray at some point. > “Never assume dependence” gets so ingrained that people stubbornly hold on to the argument in the fac

The author seems to go completely astray at some point. > “Never assume dependence” gets so ingrained that people stubbornly hold on to the argument in the face of all the common sense I can conjure. If you still disagree that assuming dependence makes more sense in this case, I guess our worldviews are so different we can’t really have a meaningful discussion. Hypothesis testing is concerned with minimization of Type I and Type II errors. In the Neyman-Pearson framework this calls for specific choice of the null hypothesis. Of course nothing prevents you to define the sets for H0 and H1 as arbitrarily as you want as long as you can mathematically justify your results. It seems like the author fundamentally misunderstands the basics of statistics.