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.
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.
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.