99% of the time, I feel like it covers subjects adequately. It might be a bit further right than me, but for a general US source, I feel it’s rather representative.
Then they write a story about something happening to low income US people, and it’s just social and logical salad. They report, it appears as though they analytically look at data, instead of talking to people. Statisticians will tell you, and this is subtle: conclusions made at one level of detail cannot be generalized to another level of detail. Looking at data without talking with people is fallacious for social issues. The NYT needs to understand this, but meanwhile they are horrifically insensitive bordering on destructive at times.
“The jackboot only jumps down on people standing up”
Hozier, “Jackboot Jump”
Then I read the next story and I take it as credible without much critical thought or evidence. Bias is strange.
This article goes through a couple cases where naively and statically conclusions are supported, but when you correctly separate the data, those conclusions reverse themselves.
Another relevant issue is Aggregation Bias. This article has an example where conclusions about a population hold inversely with individuals of that population.
And the last one I can think of is MAUP, which deals with the fact that statistics are very sensitive in whatever process is used to divvy up a space. This is commonly referenced in spatial statistics but has more broad implications I believe.
This is not to say that you can never generalize, and indeed, often a big goal of statistics is to answer questions about populations using only information from a subset of individuals in that population.
All Models Are Wrong, Some are Useful
George Box
The argument I was making is that the NYT will authoritatively make conclusions without taking into account the individual, looking only at the population level, and not only is that oftentimes dubious, sometimes it’s actively detrimental. They don’t seem to me to prove their due diligence in mitigating the risk that comes with such dubious assumptions, hence the cynic in me left that Hozier quote.
Pretty much. I never really thought about the causal link being entirely reversed, moreso that the chain of reasoning being broken or mediated by some factor they missed, which yes definitely happens, but now I can definitely think of instances where it’s totally flipped.
I feel this hard with the New York Times.
99% of the time, I feel like it covers subjects adequately. It might be a bit further right than me, but for a general US source, I feel it’s rather representative.
Then they write a story about something happening to low income US people, and it’s just social and logical salad. They report, it appears as though they analytically look at data, instead of talking to people. Statisticians will tell you, and this is subtle: conclusions made at one level of detail cannot be generalized to another level of detail. Looking at data without talking with people is fallacious for social issues. The NYT needs to understand this, but meanwhile they are horrifically insensitive bordering on destructive at times.
“The jackboot only jumps down on people standing up”
Then I read the next story and I take it as credible without much critical thought or evidence. Bias is strange.
Can you give me an example of conclusions on one level of detail can’t be generalised to another level? I can’t quite understand it
Perhaps the textbook example is the Simpson’s Paradox.
This article goes through a couple cases where naively and statically conclusions are supported, but when you correctly separate the data, those conclusions reverse themselves.
Another relevant issue is Aggregation Bias. This article has an example where conclusions about a population hold inversely with individuals of that population.
And the last one I can think of is MAUP, which deals with the fact that statistics are very sensitive in whatever process is used to divvy up a space. This is commonly referenced in spatial statistics but has more broad implications I believe.
This is not to say that you can never generalize, and indeed, often a big goal of statistics is to answer questions about populations using only information from a subset of individuals in that population.
The argument I was making is that the NYT will authoritatively make conclusions without taking into account the individual, looking only at the population level, and not only is that oftentimes dubious, sometimes it’s actively detrimental. They don’t seem to me to prove their due diligence in mitigating the risk that comes with such dubious assumptions, hence the cynic in me left that Hozier quote.
There is a name for this: Gell-Mann amnesia effect
“Wet sidewalks cause rain”
Pretty much. I never really thought about the causal link being entirely reversed, moreso that the chain of reasoning being broken or mediated by some factor they missed, which yes definitely happens, but now I can definitely think of instances where it’s totally flipped.
Very interesting read, thanks for sharing!