[QUOTE=you with the face]
Of course they do. I’m an epidemiologist, so I know that this is true. I do it all the time.
My point, though, is that the heart of the analysis is not in the math. Any high schooler can make numbers add up to the right thing. The science is in understanding populations well enough to know which assumptions are foolish and which are reasonable.
“All other things being equal” is a foolish assumption when it comes to people, because it automatically removes the scenario from the realm of reality. A mathematician may not blink an eye at such a notion, but someone who studies animal herds would laugh at any premise that assumes such a thing. It is because populations are not equal that you see differences between them, like rape discrepancies. In this country, if you have two populations that differ by race, then they also probably differ by socioeconomic status, education, health status, life expectancy, religious affiliations, drug abuse rates, etc. To assume that they are equal in every other way except race is to ignore what we know about race and what it is associated with.
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Well, you have to start somewhere. If you go into you analysis with the statement “And there must be some strange and inexplicable factor changing something so that the numbers are crap” then you’re not going to get anything done. The trick is to remember where all your numbers came from, and which assumations are flimsier than others, and to use that to your advantage when you get to the end. I will comment more on this anon.
[QUOTE=you with the face]
Yeah, you do. If the main assumptions that you are working with are A) untested, b) completely determine the outcome of your analysis, and B) go completely against what we know is true about populations, then whatever numbers you come up with has as much practical value as used kitty litter. So why wouldn’t you ignore them? What can you do with such arbitrary malarkey?
Can I ask what you do for a living?
[/QUOTE]
Certainly; I am a computer programmer, so you can be safely assured that my career never involves me making a life-changing decision regarding anybody, regardless of their race, creed, color, sexual preference, blood type, shoe size, or anything else. So you can rest easy on that point.
What do you do with such ‘arbitrary malarky’? Well, if you actually have an untested assumption that directly leads to a known false conclusion, then the most obvious thing to do is a proof by contradiction.
Take the issue at hand for example. You’ll note that in my write-up, I actually wrote all the stuff we knew first, based on the stuff that was considered to be knowable (total population; rape rates. It didn’t matter if the numbers were real; the relationship between the factors is what is of interest here). Somewhere around the end of that I drew the conclusion that the credibility of rape claimants of different races is only statistically equal if their races’ rates of making false reports approximately equate to the rates of actual rapes for those races.
In theory, I could have stopped there. I could have simply pointed out that because it is reasonable to assume that (sans other information) rape claimants are equally credible regardless of race, we can take that in combination with my previous statement and bang! Zam! Zoom! We now know that the ratio of false rape claims to actual rate claims is appromiately constant across races.
Unfortunately, I’m not quite as quick a thinker as I look; I was caught up in the flow of the thread, the clear sides that had been formed, and the fact that I was definitely not on the side of people who think that math results (or math in general) should be waved away. So, as had been done elsewhere, I then took the assumption that rape rates were proportional to population and careened off to the ostensibly false conclusion. At the time of reaching the conclusion, I had already took long enough writing it that I was impatient to post it, and so I concluded there. It wasn’t until I was in the middle of my second post on this topic that I realized that the most obvious resolution to this would be to revise the assumption about claim rates per population per race; I alluded to that at the end of that post and stated it explicitly in the next.
I wouldn’t have gained my new more-reliable assumption if I hadn’t gone through the math, which hilighted and clarified the relationships. But I did go through the math, and feel I learned something. And that, my fine fellow hominid, is why we (or at least I) do it.