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Culture War Roundup for the week of October 30, 2023

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On the use of anecdotes and “lived experiences” to contradict statistical data.

Say for the sake of argument that you’re arguing with a left-leaning individual (let’s call him “Ezra”) on the issue of police bias. You both agree the police has a least a little bit of bias when it deals with blacks, but you disagree on the root cause. Ezra contends this is due to structural racism, i.e. that laws are created in such a way such that blacks will always bear the brunt of their enforcement. He further contends that local police departments are often willing to hire white men with questionable backgrounds in terms of making racist remarks. This inherent racism exacerbates issues of uneven enforcement, and in the worst cases can lead to racist white police officers killing unarmed black men. While you agree that black men are arrested at disproportionate rates, you claim the reason for this is more simple. Black men get arrested for more crimes because… black men commit more crimes. You cite FBI crime statistics to back this up. In response, Ezra says that the FBI data you cited is nonsense that doesn’t match up with reality, but rather is cooked up by racist data officials putting their thumbs on the scales to justify the terrible actions of the criminal justice system on a nationwide basis. After all, Ezra knows quite a few black people himself, and none of them have committed any crimes! And while none of them have been arrested, a few of them have told him stories of run-ins with the police where they were practically treated as “guilty before proven innocent”. In short, Ezra’s lived experiences (along with those of people he knows) contradicts your data while buttressing his own arguments.

Do you think Ezra’s lived experiences are a valid rebuttal here?


Yesterday I made a post on the partisan differences in economic outlook. The three main points were that 1) the US economy is doing fairly well, 2) Republicans think the economy is doing absolutely terribly, much worse than Democrats think, and 3) that most of this perception difference is because Biden, a Democrat, currently occupies the White House. I initially thought I was going to get highly technical arguments quibbling over the exact measurement of data. Economic data is highly complex, and as such, reasonable people will always be able to disagree about precisely how to measure things like unemployment, GDP, inflation, etc. It’s not particularly hard to cherrypick a few reasonable-sound alternatives that would tilt measurements one way or the other. For instance, how much of housing costs should be calculated in the inflation of consumption prices? Rent can be seen as pretty much pure consumption, but homes that are purchased also have an investment aspect to them. As such, the current inflation calculations use “owners’ equivalent rent” to account for this. Most economists think this is overall the better way to calculate inflation on this particular measure, but again, reasonable people could disagree, and getting a few of them on record saying “the current measurements are faulty” is an easy way to throw doubt on data. While I did get a few of these types of comments (example 1 , example 2), they weren’t the majority of the responses by a long shot.

Instead I got plenty of arguments about “lived experiences” which people claimed as disproving the data I cited. These weren’t quite to the level of “Chicken costs $5 more at my local supermarket, therefore all economists are liars with fraudulent data”… but it wasn’t that far off.

Don’t believe me? Here’s 9 examples:

To be clear, a few of these above examples don’t say that their anecdotes prove economists are lying, and are instead using their personal experiences to say how economic conditions feel worse, although they were typically at least ambiguous on whether they trusted their own experiences over economic data at the national level. On the other hand, there were some who were quite unequivocal that economic data is fabricated in whole or in part since the things economists say don’t match with how the economy seems in their personal lives.


Going back to the example of bias in policing that I mentioned earlier, I’d say that the vast majority of people on this forum would say that you can’t really use “lived experiences” to contradict data. Anecdotes aren’t worthless, as they can give you insight into peoples’ perceptions, or how the consequences of data can be uneven and apply more to some locations than others. But at the end of the day, you can’t just handwave things like FBI crime statistics just because you know some people that contradict the data. As such, it feels like a rather blatant double standard to reject “lived experiences” when it comes to things like racism, only to turn around and accept them when it comes to the economy.

The cop-out argument from here is to point at the people preparing the data and say that they’re the ones at fault. The argument would go something like this: “My outgroup (the “elites”, the “leftists”, the “professional managerial class”, the “cathedral”, or whatever) are preparing most of the data. Data that disagrees with my worldview (like the current economic outlook) is wrong and cooked up by my outgroup to fraudulently lie to my face about reality. On the other hand, data that does agree with my worldview (like FBI crime statistics) is extra legitimate because my outgroup is probably still cooking the data, so the fact that it says what it does at all is crazy. If anything, the “real” data would probably be even more stark!”

This type of argument sounds a lot like the controversy around “unskewing” poll results. Back in 2012, Dean Chambers gathered a fairly substantial following on the Right by claiming polls showing Obama ahead were wrong due to liberal media bias. He posted “corrected” polls that almost monotonically showed Romney ahead. He would eventually get his comeuppance on election day when Obama won handily. A similar scenario played out in 2016 when many of the more left-leaning media establishment accused Nate Silver of “unskewing” poll results in favor of Trump. Reporters don’t typically have the statistical training to understand the intricacies of concepts like “correlated errors”, so all they saw was an election nerd trying to make headlines by scaring Democrats into thinking the election was closer than it really was. They too were eventually forced to eat their words when Trump won.

While issues of polling bias can be resolved by elections, the same can’t be said of bias in our examples of racism and the economy, at least not as cleanly. If someone wants to believe their anecdotes that disproportionate black arrests are entirely due to structural racism, they can just go on believing that for as long as they want. There’s no equivalent to an election-loss shock to force them to come to terms. The same is true of economic outlooks. Obviously this is shoddy thinking.

The better alternative is to use other economic data to make a point. If you think unemployment numbers don’t show the true extent of the problem, for instance, you can cite things like the prime age working ratio if you think people are discouraged from looking for work. Having tedious debates on the precise definitions of economic indicators is infinitely better than retreating to philosophical solipsism by claiming economic data is broadly illegitimate. Economic rates of change tend to be exponential year over year, so if large scale fraud is really happening then it’s hard to hide for very long. There would almost always be other data you can point to in order to make a case, even if it’s something as simple as using night light data to estimate economic output. Refusing to do even something like this is akin to sealing yourself in an unfalsifiable echo chamber where you have carte blanche to disregard anything that disagrees with your worldview.

Reporters don’t typically have the statistical training to understand the intricacies of concepts like “correlated errors”, so all they saw was an election nerd trying to make headlines by scaring Democrats into thinking the election was closer than it really was. They too were eventually forced to eat their words when Trump won.

I mostly remember them doubling down and implying that Silver was somehow super-extra-wrong and had lost credibility by saying Trump had only a 30% chance... never mind that most other pollsters were much further off (many had it more like 1%) and didn't get the same treatment.

Everyone who estimated trump's chance of winning as being above 0% chance, was right. Things with a 30% chance of happening, or a 1% chance of happening, still happen. Nate Silver and all the other pollsters would only have gotten their comeuppance if Harrison Ford had won.

I mean, there's tricky questions (both practical and philosophical) about what probability statements even mean when we're talking about singular events. But that doesn't change the fact that (a) they do sometimes help us make useful predictions, at least in the aggregate, and (b) there's a tolerably clear sense in which Silver was less wrong than someone who had Trump at 1%.

Yeah, when everyone is claiming up to election day that Trump has no chance, and he says, "no, Trump Is Just A Normal Polling Error Behind Clinton,", and then that exact thing happened, people should not be dumping on him.

Agreed, while Nate Silver wasn't a great pundit in 2016 especially during the primary, his model was better than basically anything else with a track record.

Silver had Trump much lower throughout the election and only rated Trump higher at the last moment. It's only in the retroactive history-making that he chose to portray himself as being mostly right in hindsight. But for months leading up to the election Silver and his cohort spent a lot of time explaining how Trump had no shot whatsoever.

Based on the information available at the time, that was a reasonable claim. Trump was a highly unconventional and unpopular candidate, running against an incumbent party at a time of (relative) peace and economic stability. Silver always says, that his analyses that are 3/6/12/18 months before the election, that those analyses assume a next-day election, that he doesn't price in the chance of poll numbers changing or events occurring over the remaining time - because that IS pure punditry. Some people did, of course, anticipate the convergence in poll numbers over 2016 - but what evidence was that based on? Nothing but the same old 'vibes'.

Silver got it wrong, everybody got it wrong, the polls were wrong in 2016, but ever since there's been this contrarian strain of the discourse that wants to argue that the polls were right, or whatever. Look at some of the state polling, which was wildly wrong, in some of the swing states by 10 points or more. (Similar happened in a few of the Democratic primary states Bernie was not expected to win.) In fairness, I seem to remember Silver owning up to being partially-wrong and attempting to explain why. But it doesn't mean he was right.

I guess you can argue that polling is same-day or whatever, but if polling experts all say the same thing for six months and then at the end still don't predict the correct outcome, then what value is polling? What, Silver gave Trump 30% at the last possible moment, therefore he was "reasonable"? That's all unfalsifiable augury, because as long as there's any >0% odds of an outcome Silver """predicted""" it. It's not any different from what you dismiss as "vibes".

The polls were also wrong in 2012. In fact, the error was larger in 2012, it just didn't end up changing the result.

The point is not that the polls were right. The point is that if you want to know how people are going to vote in November of 2016, there's only so much you can determine by asking them in February of 2016. There's a limit to what polling can determine, to how accurate polls can be, and that accuracy will be better the day before the election rather than six months before. As to what the point of this kind of polling is - if you're reading it, it's for you. It fills newspapers and websites, which can then sell ads. The quality of such polls is not great - after all, you're getting it for free. There's good polling out there, but it's not being used to fill space between ads, people pay money for it so they can use it to guide campaign strategy, marketing decisions, etc.

Yes, it is in fact, very reasonable to say that there isn't enough evidence to commit to one side or another.

That's all unfalsifiable augury, because as long as there's any >0% odds of an outcome Silver """predicted""" it.

No offense, but if you don't understand statistics and probability, maybe you really shouldn't be reading Nate Silver. Statistics by it's own nature cannot give certain predictions of the future.

it was a hands-off model once initiated, what are you talking about? Yes his punditry (like every other left-of-center media person) was awfully miscalibrated during the primary. but the model only rated Trump higher at the last moment because a combination of factors led to a sizable poll bump in the final two weeks prior to the election