I’m pretty sure that one of the fundamental premises of this argument is not correct, but I haven’t done mathematical statistics so I would defer to anyone who

I’m pretty sure that one of the fundamental premises of this argument is not correct, but I haven’t done mathematical statistics so I would defer to anyone who has and says I’m wrong. The author expresses this in a variety of forms throughout the article, but for instance > Significance tests are meaningful and valid if AND ONLY IF assignment was properly randomized. OK I agree with the “if” but not the “only if” part of this implication. My intuition is that significance tests are still meaningful if a non-random method of assignment is chosen, but the bias introduced by the method is not in any way correlated with the observations under study. This leads to the whole thing the author gets into about PRNGs not being valid for assignment. But a much simpler and more deterministic method of sampling is stratified sampling, which is used all over the place for various types of statistical experiments. The random entropy of a stratified sample is nowhere near enough to be fully random, yet I haven’t seen anyone claim that all experiments done using stratified sampling have invalid or meaningless p-values. Basically stratified sampling works as follows. Say you want to study the effects of age and gender on income. One way is to just study everyone, record their age, gender and income and do your study. But that gets pretty unweildy and you may not be able to get accurate data over the whole population. What you can do instead is get a list of people, sort them by age and gender and pick a size that’s convenient. So say you have capacity to analyse a sample 1/20th the size of the total. Cool. Then you just take every 20th person on your list and that’s your sample. By the magic of stratified sampling, because your list is sorted by age and gender, the sample will more or less have the same proportions as the total population with respect to age and gender. Not random, but used all over the place for statistical studies.