> Then even for tens of thousands of people, you can still get "random" divisions of a population where, say, one half has all the T9 traits -- and thereby the

> Then even for tens of thousands of people, you can still get "random" divisions of a population where, say, one half has all the T9 traits -- and thereby the "effect" is just due to this random segmentation. This complaint reveals a lack of understanding about statistics and probability. The point is that in a well-controlled experiment, the random variation is uncorrelated with the treatment. Nobody ever claimed, or ever would claim, that control removes random variation entirely, or that it's impossible to randomly obtain a data sample that looks like a causal effect. Trying to deal with this problem is literally why the entire field of probability and statistics exists. It is not at all a valid criticism of this article or the causal reasoning behind randomized controlled trials. The article even talks about this (indirectly and clumsily) in the section about "balancing covariates". Recall the definition of a p-value: the (estimated) probability that a result as extreme as the one we observed could arise simply due to random variation in sampling, measurement noise, etc? > In the hard sciences causes can actually be controlled, by eg., literally placing your hand on some part of the experiment to stop it moving (or equivalent). This is the "third option" which is missing from the intro: actual science. No, you still need to do statistical analysis of "hard science" lab experiments too, for exactly the same reason. Experimental control is never perfect, and literally every observed data point ever is a random draw from a noisy data-generating process. > speculative science There is a lot to criticize in how science, especially (but not exclusively) social science, handles statistical rigor and the communication of results that depend on statistical analysis. But this is not that.