And if one runs with that fact alone and tries to build behaviors on it, one's likely to fall into a similar trap to the one the phrenologists fell into: assuming that a single fact is descriptive enough of the world to dictate theory or practice.
Any and every statistical study ever done on race and crime supports the FACT stated above. Per capita, it's an indisputable fact that they commit more violent crimes, raw numbers alone, no curve fitting or extrapolation.
>And if one runs with that fact alone and tries to build behaviors on it, one's likely to fall into a similar trap to the one the phrenologists fell into: assuming that a single fact is descriptive enough of the world to dictate theory or practice.
Which brings us back to my original statement. Are facts racist and sexist?
There are some facts, like the one I listed above, that are not open to interpretation.
Making a terse sociological claim from statistics is almost always open to interpretation. Statistics is the science of interpreting discrete data points, looking for patterns and relations. Lies, damn lies, and all that. The only thing that stands alone without interpretation in statistics is the input data itself, and that tells us nothing.
I suspect that for the specific claim you've made, the evidence supports the claim, but I'd want to see whether they've controlled for conflating the definition of "crimes committed" with "charged with crimes" or "convicted of crimes" to be certain. Or whether the statistics are using the Bayesian or frequentist approach (after all, the claim being made is "are more likely," and that always assumes a giant pile of unstated priors). Or or or, etc.
Even still, whether the statistical claim being made is fact is uninteresting. If one assumes it's fact, the interesting question is "What do you do with it?" In isolation, with that one fact? Nothing. It's not nearly enough info to form theory or policy. Which is why my original response to your original post was about interpretation, not fact. Interpretation is where things get interesting. Facts without interpretation are dead as rocks.
Almost, but in specific cases like above, it is not open to interpretation. It's a raw fact.
>Statistics is the science of interpreting discrete data points, looking for patterns and relations.
And organizing and presenting discrete data, without interpolation.
>The only thing that stands alone without interpretation in statistics is the input data itself, and that tells us nothing.
Wrong. Basic presentation without interpolation is a core pillar of statistics.
>I suspect that for the specific claim you've made, the evidence supports the claim, but I'd want to see whether they've controlled for conflating the definition of "crimes committed" with "charged with crimes" or "convicted of crimes" to be certain.
Even with controlling, African Americans are charged, commit, and are convicted of more crimes than Asian Americans.
>Or whether the statistics are using the Bayesian or frequentist approach (after all, the claim being made is "are more likely," and that always assumes a giant pile of unstated priors).
None of the above, just basic summary statistics.
>Even still, whether the statistical claim being made is fact is uninteresting. If one assumes it's fact, the interesting question is "What do you do with it?"
It's super interesting, considering what the OP asked.
>Nothing. It's not nearly enough info to form theory or policy.
It's more than enough info to form theory, and possibly policy.
>Which is why my original response to your original post was about interpretation, not fact.
And what I've stated above is fact, not interpretation. Back to square one.
>Interpretation is where things get interesting. Facts without interpretation are dead as rocks.
Wrong. Facts without interpretation are as alive as can be, and stand on their own.
Can you give an example? What kind of policy would you form around that statistic?
Except it is, and I just proved it above.
>You might as well measure bumps on people's heads to predict whether they want anti-violence funding
Bumps on people's heads have nothing to do with violence. This is a non sequitur.
>you'll have about as much luck as you will chasing it after skin color
That makes no sense. The fact that the group is more violent is not open to interpretation, or "chasing", as I proved above.
>manifestations in African American culture that breeds violence" (what does that even mean?).
I submit to you it's exactly as irrelevant as skin color. I submit to you if you take the population that you have described, change only the category from "African American" to another category and nothing else, and kept the same population of people with no other parameters changed, you would not see a change in the violence rate.
Believing that measurable correlation implies causation is one of the major failings that I see often encouraged in the computer science space. It underpins some of the greatest failings of machine learning.
You made claims about population and then claims about interventions that suggest that the population membership is causal. What if the causal issue is poverty? Poverty in the United States is so deeply correlated with being a member of a racial category that it is extremely hard to disambiguate effects that show up in race from effects that show up in income and asset levels. This is what I mean when I say lies, damn lies, and statistics.
Because if the cause is poverty and not "African American culture," then violence interventions for African Americans are like "get your energy level up" interventions for people who are starving. You'll waste good money treating the wrong problem. You'll also miss people that also have the same problem but don't get any intervention because they don't fit the poorly-defined template.
Which is pretty indicative of why he was fired.
Logical, rational, built from facts. The thought process used by a Harvard grad, MIT research scientist.
>Which is pretty indicative of why he was fired.
"They hated him because he told the truth."
Apparently, being a Harvard grad, MIT research scientist doesn't guarantee an understanding of how not to get fired. Oh well. Google can always replace him with another Harvard grad, MIT research scientist with a better ability to play well with others. That's the perk of being Google; you don't have to tolerate the semi-productive smart kid who alienates his co-workers with "facts" he thinks he understands but doesn't. :-p
If you wouldn't mind reviewing https://news.ycombinator.com/newsguidelines.html and sticking to the rules when posting here, we'd be grateful.
p.s. I've banned https://news.ycombinator.com/user?id=TA0X2. It's not ok to use multiple accounts to get around rate limits and whatnot, so please don't do that.
Some facts are not open to interpretation.