Eugenics Shaped Statistics
nautil.us
nautil.us
Seemed much more just an interesting history of how authority, power, and bad science can intertwine, to disastrous effect.
This is in the fourth paragraph. This is clearly a hit piece against not just science but the very idea that truth can exist.
"Science" isn't a helpless creature that can be hit or needs to be defended. It's a collection of knowledge and techniques. No technique is invalidated solely by knowledge of its origins, but an awareness of the goals and constraints under which a technique is developed can give us insight into its uses and limitations now.
The idea that we shouldn't pursue those paths of knowledge, out of a responsibility to preserve the myth of science's purity from political considerations, is a radical ideology. Attempting to hide these ties, or refusing to address them, does not serve the ideals of truth that you're implying compel you.
Also, what are the issues with statistical significance? The article just says it has some connection with eugenics and leaves it there.
You figure out that all people of $ethnicity perform worse in $test? Very likely you just measured their socioeconomic background instead of the correlation of their ethnicity to that test. There is soo many ways in which such a statistical analysis can be methodically wrong, it is not even funny anymore. To find the truth here you need to be someone who cares about the truth more than a out finding proof for what they already believe, and those people are sadly quite rare.
If T(X) is a statistic designed to measure a quantity A, then significance testing are statements that say
P(T(X) is close to A | A) = P
In other words, the statistic T is close to the true value of A (which is fixed) with probability P. So far, I think this is pretty straightforward, but the tricky part comes when you apply the idea that "closeness" is symmetric. In the physical world, if A is close to B then B is close to A, but that isn't necessarily a valid thing to infer based on a significance test.
The thing ostensibly implied by a hypothesis test is that the these two are reasonable to exchange:
P(T(X) is close to A | A) = P (A is close to T(X) | T(X))
The interpretation of the right side is what you really want. The probability that the thing I want to know about is close to my statistic given my statistic. Unfortunately, this is not true in general. It's this misunderstanding that's at the heart of "logical fallacy" of frequentist hypothesis testing. No matter how careful we statisticians are about p-values, empirical evidence has shown that this leap is what people make time and time again (including us).
This is to say nothing of the absurdity of interpreting probability as a long run frequency for events that happen once or relying on results that formulate things that way when you're doing iterative analysis and development (as all actual inferential processes are).
Why do I care about the properties of my test if it doesn't say anything about the what I should know about the thing I'm measuring in this specific case. Covering the true parameter 95% of the time is useless if I can't make that leap to saying this one contains it 95% of the time.
It's so confusing, and I say this as someone who has devoted all of their adult life to statistics.
The problems with null hypothesis significant testing (NHST) are a direct result of two things:
1. the false air of objectivity it provides
2. the lack of consideration of the relative likelihood of the data under any alternative
I also agree that Pearson, Fisher, and Neyman knew what they were doing when they appealed to these methods' "objectivity" to support their terrible views.
I also find it just absolutely hilarious that Fisher's adamant opposition to Bayesian inference was the (in his opinion) absurdity of the uniform prior distribution, and yet one of his most famous contributions (Maximum likelihood estimation) is essentially the same result you'd get from the Bayesian estimate of the most likely parameter with a uniform prior.
If anyone is interested in the history of this stuff along with a discussion of the philosophical meaning of probability from the same author, his book Bernoulli's Fallacy is a pretty good read. My only qualm with it is the lack of much exposure on how to actually do Bayesian inference or a discussion on what it's like to do it in a modern setting. There's a brief few pages at the very end that mention things like Markov Chain Monte Carlo and the fact that most modern Bayesian inference is necessarily numerical approximations to the posterior, not the analytical solution (but that's not necessarily a bad thing nor is it an escape-able problem).
If you want a book that makes many of the same philosophical arguments about probability but also backs it up with math and descriptions on how to do Bayesian inference, Edwin Jaynes's Probability Theory: The Logic of Science is for you. It's quite literally a modern cult classic. People that read this book tend to go out and evangelize for it.
My final comment is that there is a place for frequentist methods, even if you're a modern Bayesian. The theory of Markov Chain Monte Carlo (one of the key methods in making modern Bayesian inference work) is exactly the domain of frequentist probability: Long runs of repeated things. Many of the diagnostic tools for assessing whether the inferences of MCMC results can be trusted are hinged on results from the frequentist interpretation and are steeped in their strange-ness. For instance, the lack of an issue is taken lack of evidence of an issue rather than evidence of no issue. The interconnected-ness of the two is unfortunately missing from the author's work (that I have seen).
Here's something he publisehd on it https://threeplusone.com/pubs/Crooks2005a.pdf https://threeplusone.com/pubs/Price2005.pdf
1. Some of the founding figures of modern statistics held objectionable moral views. He would like to cancel them for that, but can't because they were such central contributors to the field. OK, I guess nothing more to say about that.
2. There are technical reasons for objecting to NHST. You seem well read on that and I probably agree with your position.
3. Eugenics is bad. However, eugenics is bad because of its moral position, not because of faulty scientific work. The field of animal husbandry is well developed and scientifically sound. Eugenics is the application of the principles of animal husbandry to humans. It is not the science that is necessarily bad, but the motivation of its application. This is not to deny that there may have been poor quality work done in the field of eugenics.
4. Some of the work published in eugenics may have been the result of sloppy or dishonest analysis. This could be true, but again, this is not what makes eugenics bad. Also, the fact that we can evaluate research as being sloppy or dishonest indicates that we do have objective standards for acceptable work.
I think it's also more representative of the author's view that he believes that it was precisely the extreme conviction that white, European society was morally, culturally, and genetically superior that led to the development of methods that were devoid of a preference for position (and thus completely objective) that led to many of the confusing methods in use today.
I also think there's a useful distinction to be made between the terms "genetics" and "eugenics". I don't think anyone would argue that there aren't meaningful genetic differences between different groups of humans. It's the degree of extremity and the sheer horror of the conclusions drawn and methods advocated for by eugenicists that makes the distinction. Like yes, an increased probability of sickle cell anemia is a direct result of a population's adaption to an environment rife with Malaria, but that doesn't justify (I'm vaguely paraphrasing Pearson or Galton from memory here) the genocide and replacement of those people with those more capable of exploiting the natural resources of their environment (meaning white Victorian aristocrats, or at least slaves "employed" by white, Victorian aristocrats).
It's interesting to see conversations around human genomic differences rise up again with things like CRISPR, and I guess that would fall under "eugenics" but it's clearly not the same thing.
I also take issue with the idea that "it's not the science that's bad" in even tacit defense of early 1900s eugenicists. It's very telling to read the descriptions of the studies and conclusions that Galton, Pearson, and Fisher performed. It's telling to watch them discount clear counter-examples or re-define the desirability of traits, to watch them call an aristocrat industrious and a Jewish migrant greedy, to watch them all but spell out exactly what their conclusions are, develop methods "devoid of opinion" and of "pure objectivity through data," and then unabashedly call for the justified forced sterilization, mass genocide, and the denigration of non-white, non-aristocratic, non-male, non-Europeans as deterministically inferior.
Sorry for the rant. I wasn't aware of the horrific history of statistics when I decided to take it up, and now I take it as a person mission to fight against the "let the data speak for themselves" attitude. I want to make sure people know what "objective in-difference" with implicit assumptions can justify.
Maybe more to the point of your points :D :
1. Yea, not much more to say about that.
2. We do agree based on our conversation elsewhere in these threads.
3. It is bad because of both its immoral position and its faulty scientific work. The justification is equivalent to that of any of the latest unrepeatable studies on power posing and finite will power.
4. Again, it was intentionally misleading (as much as someone wholeheartedly convinced of something is misleading), and doesn't stand up to even contemporary criticisms (IMO). I do think we have objective standards, but I don't think those objective standards are reflected or implied by NHST.
Nor do I think a modern person using NHST is somehow a eugenicist by transitivity. It's more that my position is NHST has infected the scientific community precisely because of its self-reported objectivity. I think it's my field's (statistics) personal failure to bear. I think we stayed content too long as the dispensers of "objective methods" without really doing a good job of stressing the nuances and frailties associated with any particular method.
> I also think there's a useful distinction to be made between the terms "genetics" and "eugenics".
Yes, I was thinking of that point. Part of the problem in discussing "eugenics" is that it is commonly used to cover two projects. One is to understand the nature of the inheritance of physical traits, what we call "genetics". The other is to use that understanding to pursue population management, what we call "animal husbandry" in one context.
And if they do look at the data and try to fit their preconceived idea then that is P-hacking, which is statistical fraud.
That said, I find the technical arguments against NHST to be just as necessary (and I also find them just as convincing).
I'm curious what your actual objections are to this?
Eugenics is like a strange attractor for scientists: over and over again, smart quantitative people who learn about genetics, selective breeding, and human phenotypes all end up converging on the idea of using selective breeding humans to "improve the phenotype" in their biased and preconceived notion of what "improve" and "phenotype" mean. It's not at all surprising that Galton (one of my personal heros for his statistical work; I made a Galton board) and Fisher (not one of my favorites) both explored these ideas alongside their scientific efforts.
If you consider the context, I think the author's position that the confusing aspects of it are very likely because of the desire to (falsely) profess that we have removed all aspects of judgemental decisions from a particular procedure which was driven by the desire to convince people that it wasn't their opinion, but fact, that they were genetically superior beings.